List of Tables and Figures
Bibliographic record
Abstract
LIST OF TABLES 1.1 Survey respondents by gender 23 1.2 High school student respondent characteristics 23 1.3 Teacher respondent characteristics 24 2.1 Type of Internet connection at home by rural-urban location in Nova Scotia 42 2.2 Average time spent on different computers by rural-urban location and type of connection in Nova Scotia 43 2.3 Average skill levels and self-reported competence, Nova Scotia, by rural-urban location and home connectivity 44 2.4 Multiple regression analysis of (a) computer skills and (b) self-reported competence, Nova Scotia 45 2.5 Home access to ICT by north-south and rural-urban location 46 2.6 Home access to ICT by rural-urban location and ethnicity in Nunavut 47 2.7 Multiple regression analysis of home ICT access in Nunavut, all respondents and Inuit only 48 2.8 Hours spent using ICT by rural-urban location and ethnicity in Nunavut 49 2.9 Multiple regression of total time spent on computers in Nunavut, all respondents and Inuit only 50 2.10 Mean skill levels by rural-urban location and ethnicity in Nunavut 51 2.11 Multiple regression of self-reported computer skills in Nunavut, all respondents and Inuit only 52 2.12 Multiple regression of self-reported competence with ICT in Nunavut, all respondents and Inuit only 53 3.1 Mean ICT competence, skill, and disposition by cultural identity 70 3.2 Traditional culture and the Internet 76 3.3 Communication via the Internet by first language 77 4.1 Most common use of the Internet by gender for Nunavut and Nova Scotia 94 4.2 Interpersonal communication technology use of at least a few times a week by gender for Nunavut and Nova Scotia 95 vii List of Tables and Figures
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.797 | 0.566 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".