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Record W4240893514 · doi:10.1002/bult.2013.1720400101

Editor's desktop

2013· article· en· W4240893514 on OpenAlexaboutno aff
Irene L. Travis

Bibliographic record

VenueBulletin of the American Society for Information Science and Technology · 2013
Typearticle
Languageen
FieldComputer Science
TopicInformation Architecture and Usability
Canadian institutionsnot available
Fundersnot available
KeywordsCraftArchitectureFocus (optics)Advice (programming)Computer scienceLibrary scienceSociologyEngineering ethicsEngineeringVisual artsArt

Abstract

fetched live from OpenAlex

Education, practice and theory. All three of these aspects of information architecture (IA) are covered in our annual IA issue “Information Architects: What We Do and How We Learn.” In our lead article, Robert Glushko presents ideas about organizing as a general, interdisciplinary problem from his recent book The Discipline of Organization. He introduces a framework for analyzing organizational problems and systems, touching also on the problems of teaching students from heterogeneous backgrounds in a single course. The challenges of teaching IA, where interdisciplinarity is so pervasive, are also treated by Craig MacDonald and Thom Haller, our associate editor for IA. MacDonald conducted research to determine the sources from which practicing IAs say they learn their craft, while Haller explains the approach he used for 15 years to teach IA. Finally, we cover practice with both John Heffernan and Paula Land giving advice about preparing and executing system migrations. Interdisciplinarity is also the focus of the RDAP Review, as Inna Kouper, Katherine Akers and Matthew Lavin discuss their highly varied programs and goals as Council on Library and Information Resources (CLIR) data curation postdoctoral fellows. In common with information architecture, RDAP is a diverse activity that attracts and requires input from many different perspectives. ASIS&T itself is the focus of the rest of the issue. In his last column as ASIS&T 2013 president, Andrew Dillon reflects on the Association's recent accomplishments and the way in which programs must and do carry across multiple years, a process aided by our system of having a president-elect, a president and a past president who are active in the governance of the Association. We also include reports in Inside ASIS&T on programs that the organization has sponsored or co-sponsored: a doctoral forum in July at the conference of the International Society of Scientometrics and Informetrics in Vienna, Austria (Christian Schlögl); ASIS&T's participation with the AAAS Science and Human Rights Coalition (Toni Carbo); and a panel of ASIS&T Board Members discussing information science research with students at McGill University during the Board's July retreat in Montreal (Rhiannon Gainor).

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)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.553
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0090.005
Open science0.0030.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.5530.429

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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2013
Admission routes1
Has abstractyes

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