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Record W2157219361 · doi:10.1145/1753846.1754177

The future of FLOSS in CHI research and practice

2010· article· en· W2157219361 on OpenAlexaff
Paula M. Bach, Michael Terry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEnablingOpen source softwareOpen sourceJoin (topology)Knowledge managementComputer scienceWorld Wide WebSoftwareBusinessPsychologyMathematics

Abstract

fetched live from OpenAlex

In the past 10 years, Free/Libre/Open Source Software (FLOSS) has become a potent enabler in all areas of computing. Despite its rise in importance, the CHI community has been slow to study and partner with the FLOSS community. This workshop will join researchers and practitioners from the CHI and FLOSS communities to establish an agenda for future research and collaboration between the two communities.

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)0.122
metaresearch head score (Gemma)0.251
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.646

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1220.251
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0100.044
Scholarly communication0.0170.025
Open science0.0030.013
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0170.003

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 head0.034
GPT teacher head0.378
Teacher spread0.343 · 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 designTheoretical or conceptual
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
Published2010
Admission routes1
Has abstractyes

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