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Record W2417354305 · doi:10.1002/pra2.2015.14505201004

Envisioning our information future and how to educate for it: A community conversation

2015· article· en· W2417354305 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConversationAction (physics)Session (web analytics)Information technologyEngineering ethicsKnowledge managementPsychologyPublic relationsSociologyComputer sciencePolitical scienceEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

ABSTRACT Members and others attending the 2015 Association for Information Science and Technology (ASIS&T) conference will be aware of a need to regularly revisit and redefine an information discipline continuously in flux. Constant change likewise demands that we consider new models and approaches to educating professionals equipped with cutting‐edge skills in critical thinking and applied best practice responsive to a dynamic information environment. This proposed three‐segment interactive panel session will report on action research on, and findings emerging from, a re‐visioning of information education. Initial outcomes from pilot projects involving the design and testing of innovative proofs of concept will also be discussed. Attendees will engage in an activity that identifies trends, and debates issues and controversies that are at the core of the dialogue surrounding our information future and how to educate for it.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.011
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.279
Teacher spread0.255 · 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