MétaCan
Menu
Back to cohort
Record W4255169483 · doi:10.37514/per-b.2011.2379.2.18

Representing Writing: A Rhetoric for Change

2011· book-chapter· en· W4255169483 on OpenAlexfundno aff
Roger Graves

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRhetoricSociologyPolitical scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

For as long as human beings have used it to organize and conduct their activities, writing has played an integral role in the creation, sharing, and contestation of knowledge. Tracing the intertwined history of writing and secular knowledge of civilizations in Europe, the Middle East, the Mediterranean, China, India, and Mesoamerica, and Europe, Bazerman and Rogers (2008a, b), for example, map out the complex ways in which writing has been instrumental to the formation of knowledge institutions, disciplines, and communities. In the last few decades, however, the question about the role of writing in the production of knowledge has gained new salience with the rise of what has commonly been termed the knowledge society, where civic life as well as much economic activity depend on the production and sharing of knowledge. Indeed, according to some estimates, knowledge accounts for about three fourths of the value produced in the knowledge economy (Neef, 1998, ctd. in Brandt, 2005), rendering it "more valuable than land, equipment, or even money" (Brandt, p. 167). And because much of this knowledge is created, shared, inscribed, contested, and used largely through various textual forms, writing has moved centre stage in all sectors of society.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.965
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.406
GPT teacher head0.454
Teacher spread0.049 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicInnovative Education and Learning PracticesFrench-language works237,207