Eileen Younghusband Memorial Lecture
Bibliographic record
Abstract
I would like at the outset to acknowledge the traditional owners and custodians of the land on which we are meeting, the Mohawk People. The indigenous peoples of the world are victims of the colonising and oppressive practices of so-called “development ” which have culminated in economic globalisation, and their stories and their wisdom remind us of the need to reconstruct our ideas of what it means to live in one world in a more inclusive and non-colonialist way. My comments today are intended in that spirit. I also wish to express my regret that I am only able to speak in English, the language of globalisation, and to acknowledge that all three conference languages — English, French and Spanish — are languages of colonial domination. Our language effectively perpetuates the colonisation of the indigenous peoples of the world. It is a great honour and privilege to have been invited to deliver the Younghusband Lecture; indeed I can think of no higher honour for a social work educator, and I would like to thank IASSW for the invitation. If you will forgive a brief personal indulgence, it is also a particular privilege for me to be giving this lecture in Montreal. It was in the
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.029 |
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".