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Record W2974209399 · doi:10.18438/eblip29512

“Don’t Make Me Feel Dumb”: Transfer Students, the Library, and Acclimating to a New Campus

2019· article· en· W2974209399 on OpenAlexvenueno aff
Matthew Harrick, Lee Ann Fullington

Bibliographic record

VenueEvidence Based Library and Information Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConfusionComputer scienceProcess (computing)Information transferFocus groupPsychologyMedical educationSociologyMedicine

Abstract

fetched live from OpenAlex

Abstract Objective – This qualitative study sought to delineate and understand the role of the library in addressing the barriers transfer students experience upon acclimating to their new campus. Methods – A screening survey was used to recruit transfer students in their first semester at Brooklyn College (BC) to participate in focus groups. The participants discussed the issues they encountered by answering open-ended questions about their experiences on campus, and with the library specifically. Results – Transfer students desired current information about campus procedures, services, and academic support. They often had to find this information on their own, wasting valuable time. Students felt confused and stressed by this process; however, strategic library involvement can help alleviate this stress. Conclusion – Involving the library more fully in orientations could ease students’ confusion in their transitional semester. Students desired local knowledge, and the library is in a key position to disseminate this information.

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)0.013
metaresearch head score (Gemma)0.028
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.012
Scholarly communication0.0090.007
Open science0.0030.009
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.343
Teacher spread0.325 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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