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Record W2606003829

The Importance of Care in the Publishing Process

2017· article· en· W2606003829 on OpenAlexaboutno aff
Lynn Butler-Kisber, Mary Stewart

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicPublishing and Scholarly Communication
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)PublishingComputer scienceBusinessPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Lynn Butler-Kisber, McGill University Mary Stewart, LEARN Quebec The Importance of Care in the Publishing Process In this paper we will highlight with stories how, when care is integrated into the various steps in the publication process of qualitative work, it creates a thoughtful dialogue, enhances the ultimate product and scaffolds learning about both content and methodology, without sacrificing quality. Much has been written about the importance of the care in educational contexts (Noddings, 2005). Care refers to “a set of relational processes that foster mutual recognition and realization, growth, development, protection, empowerment, and human community, culture and possibility”—in learning situations (Owens, Ennis, 2005, p. 392). However, little attention has focused on the role that care can play in the publication process. Publications are frequently tied to outcomes such as promotion and tenure with little consideration about how the actual process can contribute to the development of both practitioner and academic authors and their participants. Care in the publication process contributes to how qualitative research gets delivered accessibly, transparently, and poignantly. References Noddings, N. (2005). The challenge to care in the schools. New York: Teachers’ College Press. Owens, L. M., & Ennis, K. D. (2005). The ethic of care in teaching: An overview of supportive literature. QUEST, 57, 392-425.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0040.003
Open science0.0030.000
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 head0.049
GPT teacher head0.250
Teacher spread0.201 · 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.

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
Published2017
Admission routes1
Has abstractyes

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