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Making & Doing

2021· book· en· W3177981463 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe MIT Press eBooks · 2021
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsScholarshipWork (physics)Political scienceKnowledge productionPublic relationsSociologyKnowledge managementEngineeringLawComputer science

Abstract

fetched live from OpenAlex

How ten making & doing projects expand STS scholarship through a focus on knowledge expression and knowledge travel in addition to knowledge production. Making & doing projects expand STS scholarship to include the trajectories of STS knowledge flow beyond the boundaries of the field by actively interweaving knowledge expression and travel with knowledge production. In this edited volume, contributors from around the world present and critically assess ten empirical making & doing projects. They recount how their projects advance STS, and describe how they themselves learn from their interlocutors and the settings in which they do and share their STS work. A coda explains how the infrastructures of STS scholarship are broadening to include practices of making & doing. The contributors examine and reflect upon their dilemmas, frustrations, and failures, especially when these generate new practices that might not have occurred had their work not taken the form of making and doing scholarship. While each project raises a distinct set of scholarly issues, all of the projects include practices that express STS knowledge through “STS sensibilities” and attach those sensibilities to practices in empirical fields. The projects include one each in Argentina, Taiwan, Canada, and Denmark; two in the US; one in Austria, the UK, and multiple countries in Africa and Asia; one in the US and Latin America; one in the Netherlands and Australia; and one in an international network that includes members from Europe, the Americas, and Australia. The open access edition of this book was made possible by generous funding from Arcadia – a charitable fund of Lisbet Rausing and Peter Baldwin. Contributors Gary Lee Downey and Teun Zuiderent-Jerak; Yi-Ping Lin and Hsin-Hsing Chen; Dawn Nafus, Michael Guggenheim, Judith Kröll, and Bernd Kräftner (with Alexander Martos); Hernán Thomas, Lucas Becerra, and Paula Juárez; Torben Elgaard Jensen, Andreas Birkbak, Anders Koed Madsen, and Anders Kristian Munk; Max Liboiron, Emily Simmonds, Edward Allen, Emily Wells, Jess Melvin, Alex Zahara, and Charles Mather; Jessica Mesman and Katherine Carroll; Nicholas Shapiro

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.077
GPT teacher head0.250
Teacher spread0.173 · 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