MétaCan
Menu
Back to cohort
Record W3128484755 · doi:10.29173/iasl7569

Getting Past "Shsssh": Online Focus Groups as Empowering Professional Development for Teacher Librarians

2021· article· en· W3128484755 on OpenAlexvenueno aff
Marcia A. Mardis, Ellen Hoffman

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupProfessional developmentEmpowermentOnline communityQualitative researchSociologyFocus (optics)Community of practiceFaculty developmentPedagogyPsychologyMedical educationComputer scienceMedicinePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This methodological review reports how online focus groups not only benefit the qualitative researcher but also provide professional growth among participants. The authors suggest that for school librarians, who often have limited opportunities for tailored professional development, focus groups can rapidly build a community of practice that transcends the original purpose of the researcher, enhancing knowledge and empowering new actions within schools following the model of Denzin (1997). By providing interaction, self-reflection, and professional sharing, the online focus group is a potentially powerful tool to include educators who share a professional culture but would not easily be reached by in-person techniques, as well as a method through which researchers can foster meaningful beneficial opportunities for professional learning and empowerment.

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)0.079
metaresearch head score (Gemma)0.088
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.088
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0030.003
Scholarly communication0.0050.009
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.073
GPT teacher head0.400
Teacher spread0.327 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicFocus Groups and Qualitative MethodsFrench-language works237,207