CONVERSATIONS ON CONVERSING IN CHILD AND YOUTH CARE
Bibliographic record
Abstract
In the spring of 2011, we had the pleasure of participating in the 3rd Child and Youth Care (CYC) in Action Conference hosted by the School of Child and Youth Care at the University of Victoria, Victoria, British Columbia, Canada. We were invited by conference chairs Veronica Pacini-Ketchabaw and Jennifer White to participate in a roundtable discussion on the theme of “Conversations on Conversing in Child and Youth Care”. This theme was inspired in part by a recent posting to the CYC-Net listserv, which asked, “Why are people speaking about the field in ways I don’t understand?” Veronica and Jennifer sensed that this question – and the spirited, and at times fractious, discussion that it generated on the listserv – would provide an excellent platform for mutual learning, critique, and reflection. Thus they capitalized on the opportunity to extend a conversation that was already underway, and used the question as a departure point for our roundtable discussion. In this paper, four of us who participated in the roundtable continue this conversation, with each of us probing deeper and pushing further along the themes and ideas we discussed in Victoria. We are not so much responding to any particular questions here, but rather trying to articulate some of our critical reflections on the field as we each are experiencing it. We hope that readers might engage with some of ideas we present in this conversation on their own terms.
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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 | 0.039 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.057 | 0.032 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 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".