dataesr/scientific-paragraphs-categorization huggingface dataset:
We conducted two surveys and collected data from two colleges in India using a convenience sampling approach. Oral consent was obtained from respondents by informing them about the details of the research, such as the objectives, procedures, and outcomes. To minimise the possibility of common method variance (CMV), we explicitly informed respondents that participating in the survey was voluntary. The respondents had the option not to participate or to cancel their participation at any time during the data collection. We also informed the respondents that the data would be anonymised, and they would not be identified with their names or other personal information. The first dataset contained completed surveys from 482 respondents (Study B). Of these, three were deleted because they were incomplete.
Therefore, responses from 479 students were used to test the research model. Approximately 61 % of the respondents in the first dataset were male, and the average age of the participants was 24 years. In the second dataset (Study C), 622 students answered the survey, but four responses were incomplete. Thus, we had a sample size of 618 for testing our research model. Approximately 65 % of the respondents in the second dataset were male, and the average age was 23 years. The individual responses obtained in data collection were the unit of analysis, and the sample size was considered to be appropriate following the results of the G*Power analysis  (Ringle et al. , 2014). The sample characteristics reflected general SMP user profiles discussed in recent reports  (Kemp, 2022a) , indicating that Indian SMP users are predominantly male (approximately 74.
Data analysis Before the main analysis, we confirmed that the data met the assumptions of multivariate analyses for normality and multi-collinearity. We analysed the two datasets separately through SmartPLS. PLS-SEM has been prolifically used in prior information systems literature (e. g. ,  Maier et al. , 2015). Recently, scholars have suggested that PLS-SEM is highly useful for testing models aimed at theory development and are equivalently efficient to covariance-based SEM in detecting actual pathways  (Islam et al. , 2022). PLS-SEM was considered to be more suitable in our study because (i) our framework is exploratory and aimed at contextualising the S-O-B-C framework and (ii) we focus on predicting and explaining the key variables' associations  (Hair et al. , 2017a (Hair et al. , , 2017b;; Khaola et al.
, 2022). To test convergent validity, we examined the loadings of the items, composite reliabilities (CRs), and the average variance extracted (AVEs) values. We removed the items that did not meet the recommended threshold of 0. 70 for CR and 0. 50 for AVE  (Fornell and Larcker, 1981; Hair et al. , 2017a Hair et al. , , 2017b)). Appendix A shows the factor loadings for the final items taken forward for the analysis, and Table  3  shows the CRs and AVEs for both datasets. Furthermore, to evaluate discriminant validity in our datasets, we first compared the square roots of the AVEs to the correlations. Tables  4  and 5  show the correlation matrices with the square roots of the AVEs for the first and second datasets, respectively. The square roots of the AVEs for the constructs were larger than the correlations, which suggests sufficient discriminant validity among the constructs in both datasets as per recommended thresholds  (Fornell and Larcker, 1981; Henseler et al.
, 2009). Second, we looked at the heterotrait-monotrait (HTMT) matrix (see Appendix B) for both datasets. The HTMT values were below the 0. 90 threshold  (Henseler et al. , 2015) , indicating that the measurements exhibit solid discriminant validity. We also tested the data for the absence of CMV through Harman's single-factor test  (Harman, 1976) , which confirmed that a single factor did not explain most of the total variance in both datasets. Next, we performed a common method-factor test proposed by  Liang et al. (2007) , which showed that the method variance was very small compared to the substantive variance. Thus, we concluded that CMV was not a significant concern for both datasets.
Conclusion We raised and answered two RQs aimed at understanding how a student's situational triggers and addictive SMP use reduce their AP. Having conducted a qualitative study to identify the factors that fit the S-O-B-C framework in terms of the study context-i. e. , India (Study A)we collected data through two cross-sectional surveys (Study B and C) and analysed it through PLS-SEM to test our proposed research model. We raised seven hypotheses for direct and four for indirect effects,  wherein six hypotheses for main effects were fully supported, showcasing the significant impact of situational triggers on students' organismic state that precede their SMA and RAP. We found partial support for the moderating role of demotivation due to social comparison, indicating that this may also be a contextual factor driven by individual tendencies rather than a specific SMP affordance.
Theoretical and methodological frameworks This article uses the analytical framework of Béatrice Hibou on 'state privatization' and the 'discharge' process  [17]. This theory is based on a non-normative definition of the state. What the state is, is not assumed beforehand. Thus, in order to understand the state, 'one must understand the people in power, their strategies and their historical practices'  [17]. Hibou shows that these privatizations do not imply withdrawal of the state, because the use of private intermediaries remains controlled and could be a way of exercising power. Concession regimes are characteristic of this situation. Drawing on Hibou's theory of state privatization, this article contributes an original view by focusing on the discharge attempts and negotiations. During the discharge process, how do the negotiations take place and what do they tell us about state privatization? The data used in this article were collected between 2021 and 2022 during the period the agroforest policy implementation was being drawn up.
Finally, field observations and seven qualitative interviews were conducted with farmers settled in the Diambarakro gazetted forest, which is part of the more than 75% degraded gazetted forests likely to be transformed into agroforests. Qualitative interviews were also conducted with five local SODEFOR agents in charge of the management of the Diambarakro gazetted forest. This article is based on data collected during the period of conceptualization and early implementation of this agroforest policy. The research therefore relates to the design and implementation process rather than actual implementation (legal creation of agroforests by decree following the validation of management plans), which is not yet complete, with the exception of the above-mentioned pilot project which is more about testing agroforestry systems.
Acknowledgment The topic of this article was presented during the research seminar of the  SENS research unit (CIRAD ,  Paul Valéry University, IRD, France ) held on March 15, 2022. The comments we received during the seminar helped us improve it. Special thanks to  Symphorien Ongolo ,  Sophia Carodenuto  and  Boniface Komena  for their useful comments and support. We acknowledge all the people interviewed during this research.
2 The corpus and data were collected during five months of fieldwork between 2006 and 2011 and were conducted with some 25 native speakers in various villages around Gomen and in Paimboa. I warmly thank all the collaborators who participated in this research. semantics of possession types will accurately predict the membership of the 'inalienable' set of nouns either within one language or crosslinguistically"  (Nichols 1988: 568). Haspelmath (2017: 193-194 ) also predicts that the construction types are attributable to frequency asymmetries and that "additional marking is found when the possessive relationship is less predictable". The possessive constructions in ZY are now defined and analyzed against this background. Map 1: Northern New Caledonian languages  (Ozanne-Rivierre 1995: 45). Categorizing possession in Zuanga-Yuanga 1.
Acknowledgments: I wish to thank  Françoise Rose ,  An Van  linden and two anonymous reviewers for their insightful comments and suggestions that led to the present structure of this article. Research funding: This research is supported by the  LACITO-CNRS , and the research strand 3 " Typology and dynamics of linguistic systems" of the Labex EFL (Empirical Foundations of Linguistics)  ( ANR-10-LABX-0083/CGI ).
Whereas in  [24]  the results show that the passing distance decreased when motorcycles overtake a cyclist compared to cars and small trucks. Furthermore, cyclists appeared unstable when a bus (longer passing time) passed them. Another study by  [25]  shows that vehicle drivers do not provide a comfortable passing distance to cyclists in the adjacent cycling lane. In this paper, we present the instrumentation of a city bicycle in Stockholm, Sweden. The used sensors collect comprehensive dynamical and kinematical data of the bicycle with the six Degrees of Freedom (DoF) measurements, besides the inputs of the cyclists (i. e. , pedaling power and steering angle), and estimation of road profile input using the inertial method, which is applied for the first time using a bicycle. The gathering of the inputs/outputs of the bicycle-rider system allows us to understand better the connection between them to evaluate the effect of road infrastructure on bicycle stability and cyclist behavior objectively.
3 kg). The location of the instrumentation on the bicycle is detailed in the schematic in Figure  1  and described below. 1. SG-LINK-200-OEM + Hall Effect Sensor from Alliantech was used to count the number of rotations per minute (RPM) for the front wheel to calculate its angular velocity, the sensor was fixed on the front fork and connected to a 9V battery, and a magnet was attached to one of the spokes, each time the magnet passes the sensor it counts one rotation. The sampling rate was set to 250 Hz (adjustable up to 1024 Hz), and the data was logged remotely using WSDA 200 USB.
G-link-200 Triaxial accelerometer from AlianTeck is a wireless 3-axis accelerometer with ±2 to ±40 g measurement range, low noise waveform on all axes with 25 µg/Hz or 80 µg/Hz, the sampling rate is up to 4096 Hz, which was set to 500 Hz in this experiment. The sensor connects wirelessly to WSDA 200 USB to log the data. It was fixed on the front basket over the front wheel and used to measure the accelerations of the front part of the bicycle. 3. GPS (Global Positioning System): Edge 130 plus from Garmin that includes: GPS, GLONASS, and GALILEO systems to detect the position in real-time; barometric altimeter; accelerometer, and internal memory to save data from other Garmin devices such as Vector 3, speed and cadence sensors. The device was fixed on the handlebar in order to control it easily.
The laser was fixed at the same level as the data logger and around 10 cm from the center of the rear wheel. The effective working range of the laser beam is 50-3050 mm, the measuring rate is 1-500 Hz, and the reproducibility is 1 mm maximum. The data are logged using the RS-232 interface connected through a serial portal to a computer. The sampling rate is 30 Hz. 7. K2 power bank from PowerOak was used as a battery to provide power to the laser scanner and the data logger. It has capacity of 185 Wh/50000 mAh with six output ports, one 20 V/5 A, one 12 V/2. 5 A, two 5 V/2. 1 A, and two 5 V/1 A with max power of 80 W. The dimensions of the battery are 20. 6 × 13. 5 × 3. 3 cm and it weighs 1. 26 kg. 8. Speed sensor 2 from Garmin is a wireless sensor that gives longitudinal velocity and distance at all times with no magnets or other exposed parts.
It was fixed on the hub of the rear wheel and self-calibrated with Edge 130 plus using Bluetooth. The wheel circumference was set to 2. 15 m. 9. Vector 3 power meter pedals from Garmin provides dual-sensing on both pedals, each pedal weighs 316 g, and it bears a maximum rider weight of 105 kg with a measurement accuracy of +/-1. 0%. 10. Cadence sensor 2 from Garmin was fastened to the left-side crank arm to measure pedal strokes per minute. It self-calibrates with Edge 130 plus using Bluetooth. 11. WSDA 200 USB from AlianTeck is a data acquisition gateway that collects synchronized data from scalable networks of wireless sensors such as G-link-200 and SG-link-200. It has a timestamp of ±50 µs in LXRS+ and LXRS-enabled modes, wireless range up to 2 km (400 m typical). 12. Tobii Pro Glasses 2 from Tobii Pro were used to record the gaze of the participants in the experiment environment.
Signal Processing After the experimentation, the signals from all sensors were collected and classified preparing for analysis. The total number of the output signals is 17, divided as follows: seven from the data logger (six from IMU and one from GPS), four from Aliantech sensors (G-link-200 and SG-link-200), one from the laser scanner, one from the potentiometer, and four from Garmin system. The output signals of the sensors were treated through multiple phases, as follows:  Data synchronization: different acquisition systems were used as the sensors were not purchased from the same supplier, therefore, the synchronization of the output signals is essential to correlate between the measurements. The output signals started at different times, which generates a time lag that delicate the comparison between them.
In order to remedy this and eliminate the lag effect, start point synchronization has been implemented by shifting the signal to be consistent with the last one to start. All signals were processed on MATLAB. The MATLAB code was designed to predict which signal started last (as the timestamp was recorded on the real-time HH:MM:SS:sss) and delete the collected data before this time for all the other signals, and then after applying the synchronization code the signals were compared to check if they start at the same time.  Signal processing: to better exploit the measurements from the different sensors, a filtering process is necessary to remove the noise and extract the useful signal which will be analyzed later. In order to apply filters, we should know the frequency to estimate the power spectral density (PSD) of the signal using Welch's estimate method  [26]  to adjust the cut-off frequency.
B. Longitudinal Acceleration, Velocity, and Displacement Figures  7  and 8  show the longitudinal acceleration (with zoom between 859-860 s), velocity, and displacement for the front and rear wheels respectively. The acceleration was filtered using the filtfilt method, which performs zero-phase digital filtering by processing the input data both forward and backward  [27]. After applying the filter, the velocity was calculated by integrating the acceleration, the same filtering method was repeated after the calculation of the velocity in order to remove high frequencies, and finally, the displacement was calculated by double integrating the acceleration. We can notice the effect of filtering in noise removal and normalization of the signal. https://www. hos. pub We can notice that both front and rear accelerations have negative values, despite the fact that the accelerometers were moving along the positive axis direction all the time during the experiment.
Conclusion In this paper, we presented our work on bicycle instrumentation and the subsequent experimentation and processing of the output signals. The instrumented bicycle was equipped with sensors measuring the bicycle's kinematical and dynamical properties while taking into account all six degrees of freedom (longitudinal, lateral, vertical, Yaw, Pitch, and Roll). The following sensors were included: a tri-axial accelerometer, an Inertial Measurement Unit (IMU), a GPS, a potentiometer, a laser scanner, a pedaling power meter, and speed and cadence sensors. The inputs (such as steering angle and pedaling cadence) and outputs (such as the six DoFs, speed, and position) of the system were measured and logged. The collected data were then processed on MATLAB, first by synchronizing all signals as different acquisition systems were used and then by testing different filtration techniques and choosing the most suitable one to remove the noise and normalize signals.
Aim of This Contribution The goal of this article is to propound Pillai scores as a method for the intrinsic evaluation of L2 vowels. We expand on a previous exploratory study  (Mairano et al. 2019) , and apply Pillai scores to a larger cohort of participants and languages. While the original study only analysed EnglishL2 data by ItalianL1 and FrenchL1 participants, we now add EnglishL2 data by SpanishL1 learners, as well as FrenchL2 data by EnglishL1, SpanishL1 and ItalianL1 learners. Intrinsic evaluation is compared to traditional native ratings of comprehensibility and nativelikeness obtained by EnglishL1 and FrenchL1 speakers, respectively, for the two types of data analysed. Furthermore, intrinsic vs. extrinsic evaluations are compared for the FrenchL2 data, using Pillai scores computed intrinsically (i.
Aim of This Contribution The goal of this article is to propound Pillai scores as a method for the intrinsic evaluation of L2 vowels. We expand on a previous exploratory study  (Mairano et al. 2019) , and apply Pillai scores to a larger cohort of participants and languages. While the original study only analysed English L2 data by Italian L1 and French L1 participants, we now add English L2 data by Spanish L1 learners, as well as French L2 data by English L1 , Spanish L1 and Italian L1 learners. Intrinsic evaluation is compared to traditional native ratings of comprehensibility and nativelikeness obtained by English L1 and French L1 speakers, respectively, for the two types of data analysed. Furthermore, intrinsic vs. extrinsic evaluations are compared for the French L2 data, using Pillai scores computed intrinsically (i.
English L2 Data The English L2 data analysed in this article comes from the IPCE (InterPhonology of Contemporary English) learner corpus and includes 40 speakers: 15 Italian L1 learners, 15 French L1 learners, and 10 Spanish L1 learners. While the protocol for the IPCE learner corpus includes numerous production tasks (word lists, readings, and dialogues; see  Herry-Bénit et al. 2021 , for a complete description), we only used data from the read-aloud task of a newspaper article (506 words). The 15 French L1 learners were native speakers of metropolitan French (12 F, 3 M, age = 24, SD = 6. 59). Ten of them were students at the University of Lille, and five of them were students at the University of Paris 13 Nanterre. Their L2 English level ranged from B1 to C1 of the CEFR, and their age of first contact with English was 10.
French L2 Data The French L2 data analysed in this article includes Italian L1 , Spanish L1 , and English L1 learners and were taken from three different corpora, respectively-ProSeg (Delais-Roussarie et al. 2018), Coreil (Delais-Roussarie and Yoo 2010), and AixOx  (Herment et al. 2014 ). As a consequence, the L2 French data are more heterogeneous than the L2 English data. Although the protocols of the various corpora differ, all of them include read speech, which was therefore used for our analysis. The 25 Italian L1 learners (21 F, 4 F; age = 25. 28, SD = 3. 7) from the ProSeg corpus were learners of L2 French recruited at the University of Turin. Their self-reported proficiency levels ranged from B1 to C1. While the ProSeg corpus includes various tasks, for the present study we only considered the reading aloud of eight short passages.
For instance, the /i:/ in Peter was transcribed as [i:] (target sound), irrespective of the actual realisations produced by learners. For all target vowels, we extracted acoustic parameters via an ad hoc Praat script written by the first author: for English L2 vowels, we extracted F1, F2, and duration (the latter being one of the primary cues for the tense-lax distinction); for French L2 vowels, we extracted F1, F2 and F3 (the latter being an acoustic cue of labialisation). Additionally, the following fluency metrics were calculated: AR (articulation rate, i. e. , number of phonemes per second excluding pauses), SR (speech rate, i. e. , number of phonemes per second including pauses), PSR (pause/speech ratio, i. e. , total pause duration / total speech duration), APL (average pause length).
Formants were extracted from the midpoint of each vowel  (Rathcke et al. 2017 ) to minimise coarticulation effects, using the Burg method in a band lower than 5. 5 kHz for women and 5 kHz for men. Although /i:/ can be slightly diphthongised in some dialects of English, it was treated as a monophthong  (Ferragne and Pellegrino 2010). The data were saved in. csv format and imported into R (R Core Team 2023) for analysis. We eliminated vowels marked as hesitations, false starts, and misreadings. In order to address potential formant detection errors, we eliminated realisations for which F1 or F2 was beyond 2. 5 SDs from the mean of each vowel. It should be noted that some authors use filters to eliminate detection errors, (e. g. ,  Gendrot et al. 2016) , but this approach is not viable for L2 data, where some vowels may be deviant from a given L1 norm.
After this, we were left with 10,294 vowel realisations in English L2 and 27,612 vowel realisations in French L2 (see Table  1  for details). Raw values were then normalised with Nearey1's (formant intrinsic) method with the PhonTools library (version 0. 2. 2. 1,  Barreda 2015). Pillai scores were computed for every participant and for every target contrast on R, running Manovas with F1, F2 and duration for English L2 data, and F1, F2, F3 for French L2 data. The relation between Pillai scores and native ratings of comprehensibility and nativelikeness was estimated with linear mixed-effects models fitted with the lme4 package (version 1. 1. 33,  Bates et al. 2015). p values were obtained with the lmerTest library (version 3. 1. 3,  Kuznetsova et al. 2017) , and R 2 values with the MuMin library (version 1.
Intrinsic Evaluation of French L2 Vowels with Pillai Scores A similar analysis was carried out for French L2 data. Figure  3  shows Pillai scores obtained for every French L2 learner and for target vowel contrasts involving front rounded vowels /y/ and /ø, oe/, plotted with native judgments of comprehensibility and nativelikeness for the same learners. Figure  4  illustrates vowel contrasts involving nasal vowels. The same scenario applies to models of nativelikeness, where Pillai scores for /iː/ -/ɪ/ and /ɑː/ -/ae/ are the best predictors, followed by /uː/ -/ʊ/. Instead, Pillai scores for /ɜː/ -/ʌ/ were not significant predictors of comprehensibility or nativelikeness. Combined models including all three significant Pillai scores were able to fit the data satisfactorily for comprehensibility (marginal R 2 = 0.
Extrinsic Evaluation of FrenchL2 Vowels with Pillai Scores Since FrenchL1 control data are available in the AixOx corpus, we used it to perform an extrinsic evaluation of FrenchL2 vowel realisations. Although extrinsic evaluation is usually achieved via measures other than Pillai scores, it is entirely possible to use Pillai scores to measure the amount of overlap between realisations for a given vowel by one (or more) L2 learner(s) and a cohort of L1 control speakers. In this case, a smaller amount of overlap should indicate a closer-to-target (i. e. , more native-like) pronunciation. Therefore, we computed Pillai scores for realisations of the five marked FrenchL2 vowels (/y/, /ø/, /ɛ/, /ɑ/, /ɔ/) by each L2 learner, and the corresponding vowels produced by the 10   A similar scenario applies for predictions of nativelikeness, where the Pillai score for /y/ -/u/ is the best predictor.