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Early Literacy Learning for Future Library Paraprofessionals: Authentic Learning in Library Education

2022· article· en· W4311236699 on OpenAlexaffvenueabout
Alvina Mardhani-Bayne, Lisa Shamchuk

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMacEwan University
Fundersnot available
KeywordsInformation literacyLiteracyClass (philosophy)PsychologyMathematics educationPedagogyMedical educationInstitutionEmergent literacySociologyMedicineComputer scienceSocial science

Abstract

fetched live from OpenAlex

This article describes the professional learning around early literacy experienced by library paraprofessional students at a post-secondary institution in Canada. Students completed a survey to gauge their conceptions of early literacy at the beginning of a course on library services for children and young adults. These students then experienced hands-on, engaging course elements such as in-class discussions, guest speakers, and authentic assessments. At the conclusion of the course, students were again surveyed and were asked to identify course elements that contributed to their learning. Most students aligned with an emergent literacy approach to early literacy. While a comparison between the two surveys did not reveal a significant difference in terms of students’ conceptions of early literacy, multiple students identified the hands-on elements of the course as beneficial. The researchers conclude that providing authentic professional learning opportunities that include knowledge application reinforces learners’ conceptions about emergent literacy.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0080.004
Open science0.0010.012
Research integrity0.0020.003
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 head0.038
GPT teacher head0.375
Teacher spread0.337 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

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