OISE-CIDEC-CIESC 50-year relationship: Lessons learned in leadership, mentorship, partnerships, identity and innovation
Bibliographic record
Abstract
As we approach the 50th anniversary of CIESC, we heed Vandra Masemann’s call to “gather and reflect on our historical memory” and to strive to “build our identity and broaden our reach”. Data for this paper were gathered through a combination of interviews and document analysis. Interviews were conducted with 9 current and former OISE-CIDEC faculty and staff. Documents reviewed included: CIDEC newsletters, annual reports, director/co-director reports, CIE Journal and other academic journal article reviews, and book reviews. In order to trace the evolution of the relationship between OISE-CIDEC and CIESC, we undertook a chronological analysis broken into three sections: The Formative Years: CE at University of Toronto; OISE-CIECS relationship; Leadership and partnerships: OISE-CIDEC, CIESC and beyond; Issues of naming & identity (1960s-90s); Becoming Millennials: Impacts of globalization, internationalism and technology; and finally Post-50th Anniversary (2017): Taking the OISE-CIDEC-CIESC lessons forward.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".