MétaCan
Menu
Back to cohort

Ciencia, tecnología y democracia: distinciones y conexiones

2009· article· es· W1562756282 on OpenAlexaff
Andrew Feenberg

Bibliographic record

VenueAmericanae (AECID Library) · 2009
Typearticle
Languagees
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHierarchyGovernment (linguistics)EpistemologyPolitical scienceTechnology and societyPoliticsSociologySocial sciencePublic relationsLawPhilosophy

Abstract

fetched live from OpenAlex

Este artículo argumenta que a pesar de una considerable superposición, la ciencia y la tecnología deben ser distinguidas. La investigación que procura comprender la naturaleza está controlada por la comunidad de investigadores. Esto la distingue de actividades orientadas a la producción de productos bajo el control de organizaciones tales como las corporaciones y las agencias gubernamentales. Incluso donde una y la misma actividad se preocupa tanto por la verdad como por la utilidad, ésta está controlada en los dos diferentes contextos. En el artículo, se sigue esta distinción a través de la historia de la ciencia y la sociedad durante la posguerra en Estados Unidos, por intermedio de una comparación directa de varios casos y sus implicancias, y a través de la discusión sobre la estructura paradójica de las relaciones entre tecnología y sociedad. Estas relaciones constituyen una "jerarquía entramada" porque los grupos sociales se forman alrededor de las mediaciones técnicas, las cuales a su vez median y transforman. Las políticas de ciencia y tecnología difieren en que la contribución de los grupos sociales al cambio científico es mucho menos directa que en el caso del cambio tecnológico.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.993
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0070.043
Scholarly communication0.0260.013
Open science0.0010.009
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.348
Teacher spread0.313 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAmericanae (AECID Library)Same topicResearch, Science, and AcademiaFrench-language works237,207