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Record W2738119445 · doi:10.32920/25254271.v1

Roundtable: Conversations on Conversing in Child and Youth Care

2024· preprint· en· W2738119445 on OpenAlexaffabout
Sandrina de Finney, J. N. Cole Little, Hans Skott–Myhre, Kiaras Gharabaghi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityBrock UniversityUniversity of Victoria
Fundersnot available
KeywordsPsychologyChild careDevelopmental psychologyPolitical scienceNursingMedicine

Abstract

fetched live from OpenAlex

<p>In the spring of 2011, we had the pleasure of participating in the third 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.</p> <p>In keeping with the spirit of the roundtable discussion, this is a fluid, open-ended, ongoing conversation without end. In working together, we have sought to foster the conditions for open, creative, respectful, and generative conversations across our differences. In other words, we were neither looking to find answers to these questions nor were we seeking to solve problems. On the contrary, our goal was to open up space for fresh ways of thinking about our work. We also want to extend our gratitude to Jennifer and Veronica for initiating and facilitating this conversation.</p>

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.053
GPT teacher head0.373
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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