MétaCan
Menu
Back to cohort
Record W2015123963 · doi:10.1177/0002764212469364

Motivation for Open Collaboration

2012· article· en· W2015123963 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

VenueAmerican Behavioral Scientist · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of British Columbia
FundersJohns Hopkins UniversityInstitute of Museum and Library Services
KeywordsCasualRelation (database)PsychologyOpen sourceKnowledge managementSocial psychologyApplied psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This article presents an examination of motivational factors relating to contribution to the wiki OpenStreetMap, a site for voluntary geographic information. Based on a wide literature review of motivation, open source, volunteerism, and serious leisure, a questionnaire was created and completed by 444 OpenStreetMap contributors. Results of judgments of the motivational importance of 39 reasons for contribution are presented and considered in relation to models of contributory behavior for crowd- and community-based online collaborations. Positive and important motivators were found that accorded with ideas of the “personal but shared need” associated with contribution to open-source projects, co-orientation to open-source and geographic knowledge, and attention to participation in and by the community. Differences in motivation between serious and casual mappers showed that serious mappers were more oriented to community, learning, local knowledge, and career motivations (although the latter motivation is low in general), and casual mappers were more oriented to general principles of free availability of mapping data.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.073
GPT teacher head0.459
Teacher spread0.386 · 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