Redeveloping Development: Negotiating Relationships for Advancing an Indigenous Women’s Agenda
Bibliographic record
Abstract
Lou ise Clarke, Asso ci ate Pro fes sor and Head, Depart ment of Indus trial Rela tions and Orga ni za tional Behav iour, N. Murray Edwards School of Busi ness, Uni ver sity of Sas katch e wan, is co-chair of both the inter na tional and the Cana dian plan ning groups for the con fer ence.She has writ ten on labour-man age ment rela tions and is cur rently involved in com mu nity-based research on CED in Saskatoon.Isobel M. Findlay, Asso ci ate Pro fes sor, Depart ment of Man age ment and Mar ket ing, N. Murray Edwards School of Busi ness, Univer sity of Sas katch e wan, is an asso ci ate mem ber of the national Coun cil for the Advance ment of Native Devel op ment Offi cers.She has authored numer ous ref er eed pub li ca tions (books, book chap ters, arti cles, and con fer ence pro ceed ings), includ ing Real iz ing Com mu nity: Multidisciplinary Per spec tives (1995) and (with Henderson and Benson) Aborig i nal Ten ure in the Con sti tu tion of Can ada (2000).Anna Hunter, Assis tant Pro fes sor, Depart ment of Polit i cal Stud ies, Uni ver sity of Sas katch e wan, is involved in both the Aborig i nal Pub lic Admin is tra tion Pro gram and the Indig e nous Peo ples Jus tice Pro gram.Anna is a mem ber of the Ktunaxa First Nation and is the for mer co-chair of the Cana dian plan ning group for the con fer ence.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.031 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".