title: Hāwatewate

authors: Maxwell, Te Kahautu

subjects: Whakaeke

abstract: He whakamārama mō te whakaeke apakura
He whakaeke apakura tēnei hai whakamaumaharatanga ki te kotahi rau e rima tekau tau mai i te murunga whenua o Te Whakatōhea. Ka tangihia te tāwharonanga harakoretanga o Mokomoko tipuna o Te Whakatōhea. I whakapaetekatia e Te Karauna nāna i kōhuru te mihinare a Te Wākana i te 2 o Poutūterangi i te tau kotahi mano e waru rau e ono tekau mā rima (1865). Ahakoa nā Kereopa o Ngāti Rangiwewehi i patu a Te Wākana. I haramai a Te Kereopa i raro i te Paimārire (Hauhau) ki Ōpōtiki. Ko tā Te Paimārire kaupapa i tae atu ai ko te kauhau i te whakapono hou a Te Ua Haumēne. Ko ngā āpōtoro i tukuna atu e Te Ua Haumēne ko Pātara Rakataura me Te Kereopa, hoi anō ko te kauhau i te rongopai tā Te Ua Haumēne i whakahau ai. Hoi anō he take tā Te Kereopa, tā te mea i kōhurutia tana wahine me āna tamāhine tokorua ki te whare karakia i Rangiaowhia ki roto o Tainui. Nara i pau a Ngāti Apakura ki te mate, ā i patua anō tana tamāhine hoki ki te pakanga i Hairini. Koiarā tā Kereopa he rānaki i te mate o tana whānau i pau i te ahi nukarau a te Karauna. Nā tēnei patunga i a Te Wākana i riro ai te 250,000 eka o Te Whakatōhea. I tēnei rā tonu o Te Matatini i tēnei tau 2015 kua eke ki te kotahi rau e rima tekau (150) tau o te parekuranui o Te Whakatōhea mō te patunga i te tangata kotahi nā tētahi atu. Ko te kōhuru i a Te Whakatōhea me āna tamariki, āna mokopuna, ko te tāwharona i ngā tāne, i ngā wāhine, ko te ekeeke kino te rarahu i ngā wāhine me ngā tamariki kōtiro, tāne hoki. Mutu ana te rarahu ko te kōhuru i a rātou ki te pū, ki te mata o te pēneti ko te tahu rānei. Ko te urupatu anō tērā ko te tahu i ngā pā, i ngā whare tīpuna, i ngā māra, i ngā mira mahi paura, i ngā kaipuke tauhokohoko ki Tāmaki, ki Poihākena, ki Ingarangi. Ko te parekura nui tēnei o Te Whakatōhea, ko te kōhuru i tō mātou katoa, tō mātou ao, tō mātou wairua, tō mātou hinengaro, tō mātou tinana o Te Whakatōhea mō te mate kotahi a te tangata kotahi. Ahakoa he mihingare, he pūrahorua hoki he kawe o kōrero nōna ki a Kāwana Kerei mō ngā nekeneke a Te Whakatōhea ki te tautoko i a Kīngi Tāwhiao me ngā pakanga whenua i Rangiriri, i Ōrakau i te tau kotahi mano e waru rau e ono tekau mā whā (1864).

text: Hāwatewate He whakamārama mō te whakaeke apakura
He whakaeke apakura tēnei hai whakamaumaharatanga ki te kotahi rau e rima tekau tau mai i te murunga whenua o Te Whakatōhea. Ka tangihia te tāwharonanga harakoretanga o Mokomoko tipuna o Te Whakatōhea. I whakapaetekatia e Te Karauna nāna i kōhuru te mihinare a Te Wākana i te 2 o Poutūterangi i te tau kotahi mano e waru rau e ono tekau mā rima (1865). Ahakoa nā Kereopa o Ngāti Rangiwewehi i patu a Te Wākana. I haramai a Te Kereopa i raro i te Paimārire (Hauhau) ki Ōpōtiki. Ko tā Te Paimārire kaupapa i tae atu ai ko te kauhau i te whakapono hou a Te Ua Haumēne. Ko ngā āpōtoro i tukuna atu e Te Ua Haumēne ko Pātara Rakataura me Te Kereopa, hoi anō ko te kauhau i te rongopai tā Te Ua Haumēne i whakahau ai. Hoi anō he take tā Te Kereopa, tā te mea i kōhurutia tana wahine me āna tamāhine tokorua ki te whare karakia i Rangiaowhia ki roto o Tainui. Nara i pau a Ngāti Apakura ki te mate, ā i patua anō tana tamāhine hoki ki te pakanga i Hairini. Koiarā tā Kereopa he rānaki i te mate o tana whānau i pau i te ahi nukarau a te Karauna. Nā tēnei patunga i a Te Wākana i riro ai te 250,000 eka o Te Whakatōhea. I tēnei rā tonu o Te Matatini i tēnei tau 2015 kua eke ki te kotahi rau e rima tekau (150) tau o te parekuranui o Te Whakatōhea mō te patunga i te tangata kotahi nā tētahi atu. Ko te kōhuru i a Te Whakatōhea me āna tamariki, āna mokopuna, ko te tāwharona i ngā tāne, i ngā wāhine, ko te ekeeke kino te rarahu i ngā wāhine me ngā tamariki kōtiro, tāne hoki. Mutu ana te rarahu ko te kōhuru i a rātou ki te pū, ki te mata o te pēneti ko te tahu rānei. Ko te urupatu anō tērā ko te tahu i ngā pā, i ngā whare tīpuna, i ngā māra, i ngā mira mahi paura, i ngā kaipuke tauhokohoko ki Tāmaki, ki Poihākena, ki Ingarangi. Ko te parekura nui tēnei o Te Whakatōhea, ko te kōhuru i tō mātou katoa, tō mātou ao, tō mātou wairua, tō mātou hinengaro, tō mātou tinana o Te Whakatōhea mō te mate kotahi a te tangata kotahi. Ahakoa he mihingare, he pūrahorua hoki he kawe o kōrero nōna ki a Kāwana Kerei mō ngā nekeneke a Te Whakatōhea ki te tautoko i a Kīngi Tāwhiao me ngā pakanga whenua i Rangiriri, i Ōrakau i te tau kotahi mano e waru rau e ono tekau mā whā (1864).

year: 2017

title: Ethnic disparities in breast cancer survival in New Zealand: which factors contribute?

authors: Tin Tin, Sandar

subjects: Breast cancer

abstract: Background: New Zealand has major ethnic disparities in breast cancer survival with Maori (indigenous people) and Pacific women (immigrants or descended from immigrants from Pacific Islands) faring much worse than other ethnic groups. This paper identified underlying factors and assessed their relative contribution to this risk differential. 
Methods: This study involved all women who were diagnosed with primary invasive breast cancer in two health regions, covering about 40% of the national population, between January 2000 and June 2014. Maori and Pacific patients were compared with other ethnic groups in terms of demographics, mode of diagnosis, disease factors and treatment factors. Cox regression modelling was performed with stepwise adjustments, and hazards of excess mortality from breast cancer for Maori and Pacific patients were assessed. 
Results: Of the 13,657 patients who were included in this analysis, 1281 (9.4%) were Maori, and 897 (6.6%) were Pacific women. Compared to other ethnic groups, they were younger, more likely to reside in deprived neighbourhoods and to have co-morbidities, and less likely to be diagnosed through screening and with early stage cancer, to be treated in a private care facility, to receive timely cancer treatment, and to receive breast conserving surgery. They had a higher risk of excess mortality from breast cancer (age and year of diagnosis adjusted hazard ratio: 1.76; 95% CI: 1.51-2.04 for Maori and 1.97; 95% CI: 1.67-2.32 for Pacific women), of which 75% and 99% respectively were explained by baseline differences. The most important contributor was late stage at diagnosis. Other contributors included neighbourhood deprivation, mode of diagnosis, type of health care facility where primary cancer treatment was undertaken and type of loco-regional therapy. 
Conclusions: Late diagnosis, deprivation and differential access to and quality of cancer care services were the key contributors to ethnic disparities in breast cancer survival in New Zealand. Our findings underscore the need for a greater equity focus along the breast cancer care pathway, with an emphasis on improving access to early diagnosis for Maori and Pacific women.

text: Ethnic disparities in breast cancer survival in New Zealand: which factors contribute? Background: New Zealand has major ethnic disparities in breast cancer survival with Maori (indigenous people) and Pacific women (immigrants or descended from immigrants from Pacific Islands) faring much worse than other ethnic groups. This paper identified underlying factors and assessed their relative contribution to this risk differential. 
Methods: This study involved all women who were diagnosed with primary invasive breast cancer in two health regions, covering about 40% of the national population, between January 2000 and June 2014. Maori and Pacific patients were compared with other ethnic groups in terms of demographics, mode of diagnosis, disease factors and treatment factors. Cox regression modelling was performed with stepwise adjustments, and hazards of excess mortality from breast cancer for Maori and Pacific patients were assessed. 
Results: Of the 13,657 patients who were included in this analysis, 1281 (9.4%) were Maori, and 897 (6.6%) were Pacific women. Compared to other ethnic groups, they were younger, more likely to reside in deprived neighbourhoods and to have co-morbidities, and less likely to be diagnosed through screening and with early stage cancer, to be treated in a private care facility, to receive timely cancer treatment, and to receive breast conserving surgery. They had a higher risk of excess mortality from breast cancer (age and year of diagnosis adjusted hazard ratio: 1.76; 95% CI: 1.51-2.04 for Maori and 1.97; 95% CI: 1.67-2.32 for Pacific women), of which 75% and 99% respectively were explained by baseline differences. The most important contributor was late stage at diagnosis. Other contributors included neighbourhood deprivation, mode of diagnosis, type of health care facility where primary cancer treatment was undertaken and type of loco-regional therapy. 
Conclusions: Late diagnosis, deprivation and differential access to and quality of cancer care services were the key contributors to ethnic disparities in breast cancer survival in New Zealand. Our findings underscore the need for a greater equity focus along the breast cancer care pathway, with an emphasis on improving access to early diagnosis for Maori and Pacific women.

year: 2018

title: PolyLM: Learning about polysemy through language modeling

authors: Ansell, Alan

abstract: To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings. However, sense embeddings and contextualization need not be mutually exclusive. We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied. PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondly, that for a given occurrence of a word, one of its senses tends to be much more plausible in the context than the others. We evaluate PolyLM on WSI, showing that it performs considerably better than previous sense embedding techniques, and matches the current state-of-the-art specialized WSI method despite having six times fewer parameters. Code and pre-trained models are available at https://github.com/AlanAnsell/PolyLM.

text: PolyLM: Learning about polysemy through language modeling To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings. However, sense embeddings and contextualization need not be mutually exclusive. We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied. PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondly, that for a given occurrence of a word, one of its senses tends to be much more plausible in the context than the others. We evaluate PolyLM on WSI, showing that it performs considerably better than previous sense embedding techniques, and matches the current state-of-the-art specialized WSI method despite having six times fewer parameters. Code and pre-trained models are available at https://github.com/AlanAnsell/PolyLM.

year: 2021