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Record W4404172516 · doi:10.1145/3686919

Value Tensions in OpenStreetMap: Openness, Membership, and Policy in Online Communities

2024· article· en· W4404172516 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 ACM on Human-Computer Interaction · 2024
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsOpenness to experienceValue (mathematics)EconometricsSociologyComputer scienceStatisticsPolitical scienceMathematicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

The social life and long-term trajectories of online peer production communities are shaped and animated in part by value tensions that arise when distributed, heterogeneous participants are brought together into collaboration. This study of OpenStreetMap (OSM) draws upon values-based approaches to investigate how peer production communities enact their values and navigate tensions between them. We examine how conflicts within the community over the rise of corporate participation in OSM provided a stage for the articulation and enactment of community values, shedding light on the broader dynamics and trajectory of the platform and its participants. The contributions of this work include reflections on how increasing corporate participation in OSM intersects with discourses about the emancipatory potential of emerging mapping technologies, insights into the challenges of scaling membership in peer production communities, and exploring the role of values in understanding the social life and governance of online 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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0000.001
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 head0.076
GPT teacher head0.359
Teacher spread0.284 · 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