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Record W3200165699 · doi:10.29173/slw8255

Practices and Barriers of Inter-Professional Collaboration with Teacher-librarians and Teachers: A Content Analysis

2021· article· en· W3200165699 on OpenAlexvenueno aff
Paulette Stewart, Mark-Jeffrey Oniel Deans

Bibliographic record

VenueSchool Libraries Worldwide · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisWorkloadCurriculumContent analysisProfessional developmentPsychologyProcess (computing)Medical educationPedagogyMedicineQualitative researchSociologyComputer science

Abstract

fetched live from OpenAlex

Thematic content analysis was used to identify the practices, and barriers of inter-professional collaboration among teacher-librarians and classroom teachers. Twelve structured research were identified and information regarding the themes was extracted verbatim. The data collected on practices were analysed to see how they correlated with to Loertscher and Koechlin’s (2016) conceptual framework for collaboration and co-teaching among teacher-librarians and classroom teachers. The barriers were examined and analyzed to gain an understanding of suitable recommendations to alleviate same. It was discovered that the involvement of school administration in the collaborative process; the presence of curriculum integration; and the provision of a common space for teaching were important practices for successful inter-professional collaboration. On the other hand, the lack of understanding of the role of both professionals and the workload of teachers were identified as some of the barriers. It was recommended that these can be alleviated if each professional spends time to know about each other’s job description and learn how to integrate the library seamlessly into the curriculum so that the workload of the teacher is not increased.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0010.007
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.309
Teacher spread0.275 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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