1 Becoming Social Science Researchers: Learning and Enacting New Practices and Identities
Bibliographic record
Abstract
In recent years, reports have been mounting that advocate the need for university faculty renewal and expanded research capacity across jurisdictions. Elliott (2000) projected that between 25,000 and 30,000 new faculty members will be required by 2010 to replace retiring faculty members, accommodate increasing student enrolments, and expand research capacity within Canada. Reports further suggest that there are insufficient numbers of Ph.D. students and recent graduates to take up these shortfalls (Castle & Arends, 2000; Smith, 2000). Comparable concerns have also been raised in the US, the UK, and elsewhere (e.g., Marx, 2002; Recruitment and retention of staff, 2002; West, 2001). Rae (2005) argued that Ontario must double its graduate enrollment over the next decade to replace retiring faculty members, increase research productivity to compete globally, and provide space for double-cohort students who will soon complete undergraduate degrees (i.e., the extra influx of students following the phase out of the Grade 13 or OAC year in Ontario). Provincially, nationally, and internationally, the message seems consistent: more faculty members are required and therefore more graduate students are needed. However, sheer numbers are neither the only issue nor the only solution to research capacity building. Graduate programs and faculty ranks must be opened to diverse scholars, where diversity is defined broadly to include considerations about race,
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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 | 0.083 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.039 | 0.105 |
| Scholarly communication | 0.038 | 0.038 |
| Open science | 0.005 | 0.047 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".