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Record W4389944543 · doi:10.33137/cjal-rcbu.v9.40867

Why Would I Share?

2023· article· en· W4389944543 on OpenAlexaffvenueabout
Navroop Gill

Bibliographic record

VenueCanadian Journal of Academic Librarianship · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThematic analysisInformation literacyWork (physics)Knowledge managementKnowledge sharingSociologyComputer scienceWorld Wide WebQualitative researchEngineering

Abstract

fetched live from OpenAlex

Cultures of sharing and collaboration are essential to supporting instruction practices, yet there is limited literature on how these cultures are successfully cultivated in libraries. In this paper, I explore cultures of sharing and collaboration among instruction librarians in Canadian academic libraries. I report on a series of semi-structured interviews (n=14) I conducted with librarians who support or provide information literacy at their institutions. The interview data was reviewed using a thematic analysis approach (Braun and Clark 2022) and coded in NVivo. I explore the barriers and supports to sharing and collaboration as documented in the interviews. Barriers include a) instructional silos caused by the liaison model; (b) a lack of trust in sharing one’s teaching with colleagues; (c) the lack of prioritizing instruction in institutions; and (d) limited time to engage in collaborative work. The supports for sharing and collaboration include (a) intentionally building personal relationships, (b) developing a structure for sharing, and (c) having dedicated time for collaborative work. Based on these findings, practical ways sharing and collaboration can be cultivated in libraries will be explored.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.000
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 head0.096
GPT teacher head0.313
Teacher spread0.217 · 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 designNot applicable
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

Citations1
Published2023
Admission routes3
Has abstractyes

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