Trends in Canadian Sociology Master's Theses in Relation to Research Ethics Review, 1995–2004
Bibliographic record
Abstract
THIS PAPER EXAMINES TRENDS IN CANADIAN Master's theses in sociology, 1995-2004, in the course the implementation of Canada's national research-ethics guidelines (2001), using data available from ProQuest Dissertations. While there has been no decline in the number of theses completed during this period, nearly 1/4 fewer theses now involve research participants. The proportion of theses using quantitative methods shows decline; theses using qualitative methods, however, have increased significantly over time. A closer inspection qualitative theses shows an impressive increase in the proportion of theses using interviews, while the decrease in theses using field work is even more dramatic, from 40% to 5%. The decrease of theses involving field work is particularly alarming for a significant segment of sociology that must derive its material mainly from field work. Data drawn from a larger study supplement the findings in this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.034 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.044 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".