title: The Geographic Classification for Health: Methodology and classification report

authors: Whitehead, Jesse

abstract: The GCH is based on population and drive time data that was used in the development of the Urban Accessibility (UA) classification (Statistics New Zealand, 2020). The UA is in turn based on the Statistical Standard for Geographic Areas 2018 (SSGA18) which includes the urban rural 2018 (UR2018) classification (Statistics New Zealand, 2018). The authors of this report have applied a framework to the UA classification that considers a health services discourse to determine appropriate population and drive time thresholds. We have tested both the quantitative and ‘on-the-ground’ validity of the GCH, in partnership with the Ministry of Health’s National Rural Health Advisory Group (NRHAG).

The GCH is comprised of five categories, two urban and three rural, that reflect degrees of reducing urban influence and increasing rurality. The GCH applies these categories to all of New Zealand’s Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) on a scale from ‘Urban 1’ to ‘Urban 2’ based on population size, and from “Rural 1’ to ‘Rural 3’ based on drive time to their closest major, large, medium, and small* (*As defined in the Statistical Standard for Geographic Areas 2018 (Statistics New Zealand, 2018)) urban areas. Like the UA, the GCH is based on population size and density, with drive time used to indicate increasing rurality. Unlike the UA, which is a generic classification, the population and drive time thresholds used in the GCH have been developed from a health perspective, in consultation with more than 300 individuals from 20 organisations. The nature of the functional relationships between urban areas and rural surrounds have also been considered through a health lens.

In this paper we discuss concepts and issues with previous ‘generic’ urban-rural classifications being used in health research in Aotearoa New Zealand. We also describe the GCH methodology and classification, discuss limitations, and illustrate the GCH with maps.

text: The Geographic Classification for Health: Methodology and classification report The GCH is based on population and drive time data that was used in the development of the Urban Accessibility (UA) classification (Statistics New Zealand, 2020). The UA is in turn based on the Statistical Standard for Geographic Areas 2018 (SSGA18) which includes the urban rural 2018 (UR2018) classification (Statistics New Zealand, 2018). The authors of this report have applied a framework to the UA classification that considers a health services discourse to determine appropriate population and drive time thresholds. We have tested both the quantitative and ‘on-the-ground’ validity of the GCH, in partnership with the Ministry of Health’s National Rural Health Advisory Group (NRHAG).

The GCH is comprised of five categories, two urban and three rural, that reflect degrees of reducing urban influence and increasing rurality. The GCH applies these categories to all of New Zealand’s Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) on a scale from ‘Urban 1’ to ‘Urban 2’ based on population size, and from “Rural 1’ to ‘Rural 3’ based on drive time to their closest major, large, medium, and small* (*As defined in the Statistical Standard for Geographic Areas 2018 (Statistics New Zealand, 2018)) urban areas. Like the UA, the GCH is based on population size and density, with drive time used to indicate increasing rurality. Unlike the UA, which is a generic classification, the population and drive time thresholds used in the GCH have been developed from a health perspective, in consultation with more than 300 individuals from 20 organisations. The nature of the functional relationships between urban areas and rural surrounds have also been considered through a health lens.

In this paper we discuss concepts and issues with previous ‘generic’ urban-rural classifications being used in health research in Aotearoa New Zealand. We also describe the GCH methodology and classification, discuss limitations, and illustrate the GCH with maps.

year: 2021

title: Time-of-flight perception pipeline for selective green asparagus harvesting: Theory and application

authors: Peebles, Matthew Christopher Scott

subjects: Asparagus

abstract: Generally declining labour markets, coupled with a significant increase in the projected global population have raised concerns over a potential food scarcity crisis. In response to this, the agricultural industry is currently undergoing a technological revolution, especially with respect to robotic harvesting.
Green asparagus, due to its unique physiology and labour intensive harvesting requirements, has been a long-time crop of focus in this field. Despite the relatively large body of work surrounding the development of selective robotic asparagus harvesting solutions, no such machine has yet ascended to the level of being commercially competitive. A critical component of a prospective selective robotic asparagus harvester, that is missing from contemporary machines, is a robust vision system, capable of detecting, and localising, harvest-eligible spears in real-time from a commercial asparagus field.
This thesis presents a novel perception pipeline for time-of-flight images that can achieve this task, and describes the implementation of the proposed perception pipeline into two functional robotic harvesters, AHR-1 and AHR2, capable of harvesting green asparagus spears in a real-world commercial setting.
The proposed perception pipeline achieves spear detection by firstly, segmenting the soil plane from an input pointcloud of an asparagus row, generated with a time-of-flight camera. Two plane detection methods, namely RANSAC and a modified version Hyun’s method (MHM) were investigated for this purpose. A detailed evaluation of the performance of each method on cluttered scenes revealed both RANSAC and MHM to be suitable for soil plane segmentation of asparagus beds, with both methods demonstrating similar RMSE error across a variety of scenes. The stability of model predictions made by RANSAC was generally found to be lower than that achieved by MHM, particularly for high-clutter scenes. This was determined to be due to the non-deterministic nature of RANSAC coupled with high degrees of soil plane occlusion in high clutter scenes.
Following soil plane removal, non-asparagus points pertaining to rocks, weeds, and other field debris are then filtered from the scene. This is achieved by coarse filtering input points based on the output of a FRCNN model. The various FRCNN models utilised in this work were trained with a novel dataset of labelled images, collected from various asparagus farms throughout New Zealand and California, USA. Evaluation of these models revealed a typical maximum F1 score of 0.73, providing reasonable frame-by-frame identification of asparagus features.
The remaining point clusters, representing each asparagus spear in the scene, are then filtered to remove flying pixels; a typical artefact of time-of-flight imaging. A novel geometric filter, named the closest point filter (CP filter), was developed for this task. Based on laboratory testing, it was found that this filter achieved a 68% reduction in the mean standard deviation of intra-cluster distance with respect to ground-truth positions, resulting in a significant improvement in the accuracy of base point predictions.
The proposed perception pipeline is implemented inside a ROS framework, and deployed on two robotic harvesting platforms. The first platform, AHR-1, was developed as a proof-of-concept system. This system provided a wealth of knowledge which was utilised to develop a more complete prototype asparagus harvester, AHR-2. The design of AHR-1 and AHR-2 was largely informed by the shortcomings of existing asparagus harvesting robots from the literature, particularly with respect to their harvesting, and detection strategies.
The literature surrounding robotic asparagus harvesters does not provide an adequate method for objectively evaluating the performance of such systems. Consequentially, performance metrics pertaining to existing robotic harvesters are relatively opaque, particularly with respect to the selective nature of the harvesting task. These inadequacies necessitated the development of a novel evaluation method for the evaluation of a selective asparagus harvester, which was utilised to measure the performance of both AHR-1 and AHR-2.
Several field trials of AHR-1 and AHR-2 were conducted on farms throughout New Zealand and California, USA. The resulting analysis revealed that AHR-2 achieved state-of-the-art performance, harvesting 45.9% of all harvest eligible spears with a precision of 87.2% at its nominal ground speed of 0.3m/s. The vision system detected 97% of all harvest-eligible spears with a precision of 74.5% at this speed. When operating at a ground speed of 0.7m/s AHR 2’s harvesting rate fell to 22% with 95% precision. The corresponding drop in the vision systems detection rate was relatively small, dropping to 92.5% with 87.2% precision at a ground speed of 0.7m/s. From this it was concluded that the vision system outperformed the available hardware. The perception pipeline proposed by this thesis achieved state-of-the-art performance at a variety of ground speeds. The pipeline demonstrated a robustness to the unstructured nature of commercial asparagus fields, providing spear locations which successfully facilitated the robotic harvest of green asparagus spears.

text: Time-of-flight perception pipeline for selective green asparagus harvesting: Theory and application Generally declining labour markets, coupled with a significant increase in the projected global population have raised concerns over a potential food scarcity crisis. In response to this, the agricultural industry is currently undergoing a technological revolution, especially with respect to robotic harvesting.
Green asparagus, due to its unique physiology and labour intensive harvesting requirements, has been a long-time crop of focus in this field. Despite the relatively large body of work surrounding the development of selective robotic asparagus harvesting solutions, no such machine has yet ascended to the level of being commercially competitive. A critical component of a prospective selective robotic asparagus harvester, that is missing from contemporary machines, is a robust vision system, capable of detecting, and localising, harvest-eligible spears in real-time from a commercial asparagus field.
This thesis presents a novel perception pipeline for time-of-flight images that can achieve this task, and describes the implementation of the proposed perception pipeline into two functional robotic harvesters, AHR-1 and AHR2, capable of harvesting green asparagus spears in a real-world commercial setting.
The proposed perception pipeline achieves spear detection by firstly, segmenting the soil plane from an input pointcloud of an asparagus row, generated with a time-of-flight camera. Two plane detection methods, namely RANSAC and a modified version Hyun’s method (MHM) were investigated for this purpose. A detailed evaluation of the performance of each method on cluttered scenes revealed both RANSAC and MHM to be suitable for soil plane segmentation of asparagus beds, with both methods demonstrating similar RMSE error across a variety of scenes. The stability of model predictions made by RANSAC was generally found to be lower than that achieved by MHM, particularly for high-clutter scenes. This was determined to be due to the non-deterministic nature of RANSAC coupled with high degrees of soil plane occlusion in high clutter scenes.
Following soil plane removal, non-asparagus points pertaining to rocks, weeds, and other field debris are then filtered from the scene. This is achieved by coarse filtering input points based on the output of a FRCNN model. The various FRCNN models utilised in this work were trained with a novel dataset of labelled images, collected from various asparagus farms throughout New Zealand and California, USA. Evaluation of these models revealed a typical maximum F1 score of 0.73, providing reasonable frame-by-frame identification of asparagus features.
The remaining point clusters, representing each asparagus spear in the scene, are then filtered to remove flying pixels; a typical artefact of time-of-flight imaging. A novel geometric filter, named the closest point filter (CP filter), was developed for this task. Based on laboratory testing, it was found that this filter achieved a 68% reduction in the mean standard deviation of intra-cluster distance with respect to ground-truth positions, resulting in a significant improvement in the accuracy of base point predictions.
The proposed perception pipeline is implemented inside a ROS framework, and deployed on two robotic harvesting platforms. The first platform, AHR-1, was developed as a proof-of-concept system. This system provided a wealth of knowledge which was utilised to develop a more complete prototype asparagus harvester, AHR-2. The design of AHR-1 and AHR-2 was largely informed by the shortcomings of existing asparagus harvesting robots from the literature, particularly with respect to their harvesting, and detection strategies.
The literature surrounding robotic asparagus harvesters does not provide an adequate method for objectively evaluating the performance of such systems. Consequentially, performance metrics pertaining to existing robotic harvesters are relatively opaque, particularly with respect to the selective nature of the harvesting task. These inadequacies necessitated the development of a novel evaluation method for the evaluation of a selective asparagus harvester, which was utilised to measure the performance of both AHR-1 and AHR-2.
Several field trials of AHR-1 and AHR-2 were conducted on farms throughout New Zealand and California, USA. The resulting analysis revealed that AHR-2 achieved state-of-the-art performance, harvesting 45.9% of all harvest eligible spears with a precision of 87.2% at its nominal ground speed of 0.3m/s. The vision system detected 97% of all harvest-eligible spears with a precision of 74.5% at this speed. When operating at a ground speed of 0.7m/s AHR 2’s harvesting rate fell to 22% with 95% precision. The corresponding drop in the vision systems detection rate was relatively small, dropping to 92.5% with 87.2% precision at a ground speed of 0.7m/s. From this it was concluded that the vision system outperformed the available hardware. The perception pipeline proposed by this thesis achieved state-of-the-art performance at a variety of ground speeds. The pipeline demonstrated a robustness to the unstructured nature of commercial asparagus fields, providing spear locations which successfully facilitated the robotic harvest of green asparagus spears.

year: 2021

title: Te whakaako i te reo Māori i te kura auraki tuarua i Aotearoa nei: Kei tua o te awe māpere. The teaching of te reo Māori in English-medium secondary schools in New Zealand: Beyond the mask.

authors: Nock, Sophie

subjects: language curricula

abstract: He taonga te reo Māori nō tuawhakarere, nō tuauriuri, whāioio, nā te atua i homai kia poipoia e te iwi Māori hei reo kōrero mō te hunga kaingākau, mō te hunga kāore anō kia whānau noa.  Ahakoa ia he toimaha tonu te hauora o te reo Māori, nā te mahi tūkino (a ētahi).  I roto i ngā tau whā tekau kua ara ake ngā momo rongoā hei whakahauora, hei whakatairanga i te reo Māori, nā, ko Te Kōhanga Reo tērā, ko te Kura Kaupapa Māori tērā, ko Te Wharekura tērā, aha atu, aha atu.  Ka mutu, i te tau 1987 i whakamanahia ai te reo Māori hei reo tūturu o tēnei whenua.  Ahakoa he aha e mea ana te Tāhūhū o te Mātauranga ehara i te mea me whai wāhi te reo Māori hei reo ako i roto i ngā kura tuarua puta noa i te motu.  Engari, ko te raruraru kua whai wāhi kē ētahi atu reo o te ao, kaua ko te reo Māori.  Kāti, i te tau 2009 i tatū ai Te Aho Arataki Marau mō te Ako i Te Reo Māori, ā, ki reira whai wāhi ai te reo Māori.  Nā, ko tā te kura tuarua mahi he tautoko mārika i te reo Māori, mā te whakaako i te reo Māori, e tika ana me pērā, nā te mea, ko te nuinga o ngā tamariki Māori e haere ana ki a ia ako ai.  Mōhio pai i whakapeto ngoi ngā kaiwhakaako reo Māori me te matatau anō o ētahi ki te reo hoki, engari, ki tā te pūrongo rangahau a Te Tari Arotake Mātauranga kīhai i tino eke panuku ngā mahi whakaako.  Ko ngā tino take, ko te whakangungu kaiwhakaako, ko te tautoko me te whakahaere a te kura hoki.

Nō reira, ko te whāinga matua o tēnei rangahau kia tirohia me pēhea e whakaako ai te reo Māori ki te kura tuarua, (kaua i ngā wharekura) i Aotearoa.  Mai i te rau tau tekau mā waru ka tātaria ētahi o ngā kōrero whanake e hāngai pū ana ki te whakaako me te ako i (t)ētahi atu reo, ko ia te wāhanga tuarua.  Whai muri mai i tērā (kei te wāhanga tuatoru) ko ngā patapatai me ngā uiuitanga, ā, ko tā te patapatai he kukume mai i ngā whakapono, i ngā whakaaro, i ngā waiaro o te hunga kaiwhakaako, ko tā te uiuitanga e whakatātūtū i te rētotanga o te kaupapa.  Ka mutu, i whārikihia ngā whakakitenga, ngā whakahoki kōrero ki taua wāhanga.  I te mutunga iho i whakapau kaha ngā kaiwhakaako ki te whakatutuki i ngā mahi, ahakoa ngā piki me ngā heke.  Kei ngā wāhanga e rua (4 & 5) ngā kōrero mō ngā tūmomo rauemi e whakamahia ana e ngā kaiwhakaako, arā, ko ngā momo pukapuka (4) me ngā tūmomo rauemi o te ipurangi nā Te Tāhūhū o te Mātauranga i whakarato (5).  E kī ana ngā whakaputanga, tuatahi, kīhai aua rauemi i tino whakaatu mai i ngā momo whanaketanga i roto i ngā tau whā tekau neke atu mō te whakaako i te reo tuarua, hou rānei, tuarua, kāhore hoki i tino hāngai pū ēnei rauemi ki te Marau mō te Ako i Te Reo Māori i te kura tuarua me te hāngai anō hoki ki te āhua o te whakaako i te reo mā te ‘Communicative Language Teaching’.  Hei whaiwhai ake i ngā rauemi i tātaria ētahi o ngā karaehe reo (te wāhanga 6) mā ngā wehewehetanga matua o te whakaako reo.  Mai i taua tātaritanga i kitea mai ētahi raruraru, tuatahi, kāhore ngā tauira/akonga i whai wāhi ki te kōrero (i roto tonu i te karaehe), tuarua, kāhore e taea te whakawhitiwhiti kōrero ki a rātou anō, tuatoru, kāhore rātou i te mahi tahi anō hoki.  Hei whakakapi ake i tēnei rangahau, kei te wāhanga whakamutunga (7) te whakakaokao mai o ngā whakakitenga katoa, me te whakatakoto o ngā tūtohutanga kia anga whakamua ai. 

It has been acknowledged that te reo Māori is a taonga and, as such, is subject to the protections guaranteed under the terms of Te Tiriti o Waitangi.  Since 1987, it has been an official language of Aotearoa.  Its future is, however, very far from secure and instructed language learning continues to play a significant part in the revitalisation agenda.  Even so, although schools are now required to offer a language other than the language of instruction to pupils in Years 7 to 10, and although over two thirds of Māori students currently attend schools in which English is the main language of instruction, schools are not required to include te reo Māori in their language offering.  In addition, although curriculum guidelines documents for a number of foreign languages have been available for many years, it was not until 2009 that the final version of a curriculum guidelines document for the teaching and learning of te reo Māori in English-medium schools became available.  Furthermore, while there clearly are some extremely competent teachers of the language, Education Review Office (ERO) reports indicate that all is not well so far as the teaching and learning of te reo Māori in English-medium secondary schools is concerned.  The problems identified relate not to teachers' proficiency in the language but to their limited pedagogical knowledge.  As ERO has indicated, this raises questions about the initial training of teachers of te reo Māori and the ways in which all schools manage and support them. 

The overall aim of the research reported here is to investigate the teaching of te reo Māori in English-medium secondary schools in Aotearoa.  Following a critical review of selected literature on developments in the teaching and learning of additional languages since the end of the 18th Century (Chapter 2), there is a report on a survey (involving questionnaire responses and semi-structured interviews) of a sample of teachers of te reo Māori in English-medium secondary schools.  That survey focuses on their educational and linguistic backgrounds and their attitudes and approaches to a range of issues associated with the teaching and learning of the language in school settings (Chapter 3).  Overall, the findings indicate that while these teachers are doing their best to provide a high quality educational experience for their students, they are doing so in the face of a number of significant barriers relating, in particular, to the limited nature of the pre-service training they have received, and the range of additional duties they are expected to perform.  In addition, the survey revealed some concerns about the impact of national assessment on teaching and learning and about the teaching materials and resources that are available.  The next two chapters report on the analysis of a sample of textbooks which are widely used in secondary schools (Chapter 4) and a sample of resources made available by the Ministry of Education (Chapter 5), the main findings being that these are, in the main, (a) inconsistent with both research-based developments in the teaching of additional languages and the expectations relating to communicative language teaching as outlined in the curriculum guidelines, and (b) that these resources do not align with the new curriculum.  The analysis of a sample of lessons taught in secondary schools follows (Chapter 6).  That analysis, conducted in relation to a number of focus points, reveals some significant problems, including the fact that the students were provided with few opportunities to contribute, to interact with one another, and to engage in genuinely communicative activities.  The final chapter (Chapter 7) provides an overview of the findings and includes some suggestions about a possible way forward.

text: Te whakaako i te reo Māori i te kura auraki tuarua i Aotearoa nei: Kei tua o te awe māpere. The teaching of te reo Māori in English-medium secondary schools in New Zealand: Beyond the mask. He taonga te reo Māori nō tuawhakarere, nō tuauriuri, whāioio, nā te atua i homai kia poipoia e te iwi Māori hei reo kōrero mō te hunga kaingākau, mō te hunga kāore anō kia whānau noa.  Ahakoa ia he toimaha tonu te hauora o te reo Māori, nā te mahi tūkino (a ētahi).  I roto i ngā tau whā tekau kua ara ake ngā momo rongoā hei whakahauora, hei whakatairanga i te reo Māori, nā, ko Te Kōhanga Reo tērā, ko te Kura Kaupapa Māori tērā, ko Te Wharekura tērā, aha atu, aha atu.  Ka mutu, i te tau 1987 i whakamanahia ai te reo Māori hei reo tūturu o tēnei whenua.  Ahakoa he aha e mea ana te Tāhūhū o te Mātauranga ehara i te mea me whai wāhi te reo Māori hei reo ako i roto i ngā kura tuarua puta noa i te motu.  Engari, ko te raruraru kua whai wāhi kē ētahi atu reo o te ao, kaua ko te reo Māori.  Kāti, i te tau 2009 i tatū ai Te Aho Arataki Marau mō te Ako i Te Reo Māori, ā, ki reira whai wāhi ai te reo Māori.  Nā, ko tā te kura tuarua mahi he tautoko mārika i te reo Māori, mā te whakaako i te reo Māori, e tika ana me pērā, nā te mea, ko te nuinga o ngā tamariki Māori e haere ana ki a ia ako ai.  Mōhio pai i whakapeto ngoi ngā kaiwhakaako reo Māori me te matatau anō o ētahi ki te reo hoki, engari, ki tā te pūrongo rangahau a Te Tari Arotake Mātauranga kīhai i tino eke panuku ngā mahi whakaako.  Ko ngā tino take, ko te whakangungu kaiwhakaako, ko te tautoko me te whakahaere a te kura hoki.

Nō reira, ko te whāinga matua o tēnei rangahau kia tirohia me pēhea e whakaako ai te reo Māori ki te kura tuarua, (kaua i ngā wharekura) i Aotearoa.  Mai i te rau tau tekau mā waru ka tātaria ētahi o ngā kōrero whanake e hāngai pū ana ki te whakaako me te ako i (t)ētahi atu reo, ko ia te wāhanga tuarua.  Whai muri mai i tērā (kei te wāhanga tuatoru) ko ngā patapatai me ngā uiuitanga, ā, ko tā te patapatai he kukume mai i ngā whakapono, i ngā whakaaro, i ngā waiaro o te hunga kaiwhakaako, ko tā te uiuitanga e whakatātūtū i te rētotanga o te kaupapa.  Ka mutu, i whārikihia ngā whakakitenga, ngā whakahoki kōrero ki taua wāhanga.  I te mutunga iho i whakapau kaha ngā kaiwhakaako ki te whakatutuki i ngā mahi, ahakoa ngā piki me ngā heke.  Kei ngā wāhanga e rua (4 & 5) ngā kōrero mō ngā tūmomo rauemi e whakamahia ana e ngā kaiwhakaako, arā, ko ngā momo pukapuka (4) me ngā tūmomo rauemi o te ipurangi nā Te Tāhūhū o te Mātauranga i whakarato (5).  E kī ana ngā whakaputanga, tuatahi, kīhai aua rauemi i tino whakaatu mai i ngā momo whanaketanga i roto i ngā tau whā tekau neke atu mō te whakaako i te reo tuarua, hou rānei, tuarua, kāhore hoki i tino hāngai pū ēnei rauemi ki te Marau mō te Ako i Te Reo Māori i te kura tuarua me te hāngai anō hoki ki te āhua o te whakaako i te reo mā te ‘Communicative Language Teaching’.  Hei whaiwhai ake i ngā rauemi i tātaria ētahi o ngā karaehe reo (te wāhanga 6) mā ngā wehewehetanga matua o te whakaako reo.  Mai i taua tātaritanga i kitea mai ētahi raruraru, tuatahi, kāhore ngā tauira/akonga i whai wāhi ki te kōrero (i roto tonu i te karaehe), tuarua, kāhore e taea te whakawhitiwhiti kōrero ki a rātou anō, tuatoru, kāhore rātou i te mahi tahi anō hoki.  Hei whakakapi ake i tēnei rangahau, kei te wāhanga whakamutunga (7) te whakakaokao mai o ngā whakakitenga katoa, me te whakatakoto o ngā tūtohutanga kia anga whakamua ai. 

It has been acknowledged that te reo Māori is a taonga and, as such, is subject to the protections guaranteed under the terms of Te Tiriti o Waitangi.  Since 1987, it has been an official language of Aotearoa.  Its future is, however, very far from secure and instructed language learning continues to play a significant part in the revitalisation agenda.  Even so, although schools are now required to offer a language other than the language of instruction to pupils in Years 7 to 10, and although over two thirds of Māori students currently attend schools in which English is the main language of instruction, schools are not required to include te reo Māori in their language offering.  In addition, although curriculum guidelines documents for a number of foreign languages have been available for many years, it was not until 2009 that the final version of a curriculum guidelines document for the teaching and learning of te reo Māori in English-medium schools became available.  Furthermore, while there clearly are some extremely competent teachers of the language, Education Review Office (ERO) reports indicate that all is not well so far as the teaching and learning of te reo Māori in English-medium secondary schools is concerned.  The problems identified relate not to teachers' proficiency in the language but to their limited pedagogical knowledge.  As ERO has indicated, this raises questions about the initial training of teachers of te reo Māori and the ways in which all schools manage and support them. 

The overall aim of the research reported here is to investigate the teaching of te reo Māori in English-medium secondary schools in Aotearoa.  Following a critical review of selected literature on developments in the teaching and learning of additional languages since the end of the 18th Century (Chapter 2), there is a report on a survey (involving questionnaire responses and semi-structured interviews) of a sample of teachers of te reo Māori in English-medium secondary schools.  That survey focuses on their educational and linguistic backgrounds and their attitudes and approaches to a range of issues associated with the teaching and learning of the language in school settings (Chapter 3).  Overall, the findings indicate that while these teachers are doing their best to provide a high quality educational experience for their students, they are doing so in the face of a number of significant barriers relating, in particular, to the limited nature of the pre-service training they have received, and the range of additional duties they are expected to perform.  In addition, the survey revealed some concerns about the impact of national assessment on teaching and learning and about the teaching materials and resources that are available.  The next two chapters report on the analysis of a sample of textbooks which are widely used in secondary schools (Chapter 4) and a sample of resources made available by the Ministry of Education (Chapter 5), the main findings being that these are, in the main, (a) inconsistent with both research-based developments in the teaching of additional languages and the expectations relating to communicative language teaching as outlined in the curriculum guidelines, and (b) that these resources do not align with the new curriculum.  The analysis of a sample of lessons taught in secondary schools follows (Chapter 6).  That analysis, conducted in relation to a number of focus points, reveals some significant problems, including the fact that the students were provided with few opportunities to contribute, to interact with one another, and to engage in genuinely communicative activities.  The final chapter (Chapter 7) provides an overview of the findings and includes some suggestions about a possible way forward.

year: 2014