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Record W1586560978 · doi:10.5901/ajis.2015.v4n1s2p75

Negotiating the Digital Line: A Qualitative Inquiry into the Use of Communication Technologies in Professional Child and Youth Care Practice

2015· article· en· W1586560978 on OpenAlexaff
Meghan Parry, Gerard Bellefeuille

Bibliographic record

VenueAcademic Journal of Interdisciplinary Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicFlooding and Environmental Impact
Canadian institutionsMacEwan University
Fundersnot available
KeywordsNegotiationAgency (philosophy)Qualitative researchWork (physics)Child careEmerging technologiesPublic relationsEngineering ethicsPsychologyMedical educationSociologyNursingPolitical scienceMedicineEngineeringComputer scienceSocial science

Abstract

fetched live from OpenAlex

While social and communication technologies are changing the world at warp speed, little is known about how Child and Youth Care (CYC) practitioners are using these technologies in their work with children, youth, and families. This article reports findings from a qualitative study that explored potential boundary and ethical implications related to the integration of communication technologies by CYC practitioners in their professional relationships with children, youth, and families. The study also sought to examine what form of communication technologies is being used most commonly and the nature of agency policies, standards, and procedures that address the use of this technology by CYC practitioners with clients. DOI: 10.5901/ajis.2015.v4n1s2p75

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0160.021
Scholarly communication0.0080.007
Open science0.0020.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.208
GPT teacher head0.482
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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