OISE-CIDEC-CIESC 50-year relationship: Lessons learned in leadership, mentorship, partnerships, identity and innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it