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Record W2620942420

Material-discursive practices in technology standards development: A topic-modeling approach to technology evolution

2017· article· en· W2620942420 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

VenueJournal of the Association for Information Systems · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerformative utteranceDialogical selfSociologyPerspective (graphical)EpistemologyProcess (computing)Knowledge managementComputer science
DOInot available

Abstract

fetched live from OpenAlex

Social interactions play a vital role in shaping technology evolution, especially in technology standards development involving multiple actors across the industry. However, previous studies mostly focus on the interactions between technology designs and socio-cognitive factors and pay little attention to the intertwined nature of social and material aspects of technologies. From the sociomaterial perspective, a key is to focus on the process of discursive materialization and its performative consequences in practice. Drawing on Orlikowski and Scott’s (2015) material-discursive perspective, this paper examines how the HTML5 (technology standard) evolves over time by investigating the processes of discursive materialization at the World Wide Web Consortium with the topic modeling techniques. The analysis shows that four fundamental mechanisms (process management, dialogical coordination, boundary work, and knowledge conversion) shape the evolution of HTML5. This study contributes to understanding processes of technology evolution from the sociomaterial perspective with a novel empirical approach.

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)0.003
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: none
Teacher disagreement score0.881
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0020.000
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 head0.023
GPT teacher head0.299
Teacher spread0.276 · 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