MétaCan
Menu
Back to cohort
Record W3145266403 · doi:10.29173/iasl8016

MLC Libraries intranet website: A collaborative process

2021· article· en· W3145266403 on OpenAlexvenueno aff
Mark Viner

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsIntranetWorld Wide WebInformation literacyThe InternetPromotion (chess)Reading (process)NewspaperComputer scienceLibrary instructionMultimediaBusinessPolitical scienceAdvertising

Abstract

fetched live from OpenAlex

This paper examines the collaborative processes involved in developing the MLC Libraries intranet website and outlines helpful hints about website design, evaluation and promotion. The MLC Libraries intranet website has been designed to provide the MLC community with information resources that reflect the educational, cultural, literary and recreational needs of the college. Information resources include an online suggestion book, online teacher librarian research assistance, access to the catalogue, newspaper and magazine databases, reading lists, information literacy programs and internet search tips. MLC Libraries promote the development of information literacy and nurture an appreciation of literature in a supportive, creative and information-rich learning environment. This intranet website was created by a team of MLC teacher librarians and library staff, with technical support from a website designer and MLC computer staff. It was launched to the MLC community in March 2003. The workshop will demonstrate a virtual and visual tour of the MLC Libraries intranet website.

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)0.038
metaresearch head score (Gemma)0.061
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 score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0110.005
Scholarly communication0.0150.013
Open science0.0030.016
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.005

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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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 topicLibrary Science and AdministrationFrench-language works237,207