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Record W1865122083 · doi:10.18357/ijcyfs32-3201210862

CONVERSATIONS ON CONVERSING IN CHILD AND YOUTH CARE

2012· article· en· W1865122083 on OpenAlex
Sandrina de Finney, JN Cole Little, Hans Skott–Myhre, Kiaras Gharabaghi

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsToronto Metropolitan UniversityBrock UniversityUniversity of Victoria
Fundersnot available
KeywordsConversationTheme (computing)PleasureMedia studiesSociologyWhite (mutation)PsychologyCommunicationComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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 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: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.327

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.398
Teacher spread0.326 · 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