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Record W2758391748 · doi:10.5860/crl.79.4.568

Buy, Borrow, or Steal? Film Access for Film Studies Students

2018· article· en· W2758391748 on OpenAlexaffabout
Wendy Rodgers

Bibliographic record

VenueCollege & Research Libraries · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDeskClass (philosophy)Order (exchange)Government (linguistics)CurriculumAdvertisingBusinessSociologyInternet privacyComputer sciencePolitical scienceLawPedagogy

Abstract

fetched live from OpenAlex

Libraries offer a mix of options to serve the film studies curriculum: streaming video, DVDs on Reserve, and streaming DVDs through online classrooms. Some professors screen films and lend DVDs to students. But how do students obtain the films required for their courses? How would they prefer to do so? These are among the questions explored using data obtained by surveying students at one Canadian university, and faculty and librarians at ten Canadian universities that offer film studies programs. The study finds that more students are engaging in digital piracy than using the Library Reserve desk, and that faculty and librarians must contend with formats, licenses, and copyright in order to provide effective, legal access. The paper recommends that libraries increase streaming options, collect DVDs for preservation, digitize DVDs when legally possible, screen films in class, investigate social screening spaces, and lobby industry and government for changes to law and practice.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0460.004

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 head0.332
GPT teacher head0.449
Teacher spread0.117 · 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.

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

Citations32
Published2018
Admission routes2
Has abstractyes

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