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Record W2946714813

The Patent Genre: Between Stability and Change

2019· article· en· W2946714813 on OpenAlexfundaboutno aff
Fiorella Foscarini

Bibliographic record

VenueArchivaria (Association of Canadian Archivists) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsRhetorical questionNegotiationSociologyHumanitiesPolitical scienceArtSocial scienceLiterature
DOInot available

Abstract

fetched live from OpenAlex

This article examines patents as typified communicative practices enacted within the articulated and rigid boundaries of legal systems.By applying concepts from rhetorical genre studies, the article identifies the regularities of textual features and social intentions characterizing the patent genre and shows how patents are compulsorily constructed, evaluated, and contested within highly structured socio-cultural contexts.Despite their conservative nature, both patents and the rules surrounding them are the outcome of continuous negotiations of meanings and motives among those participating in them (e.g., inventors, patent examiners, and legislators) and depend on this participation.Through a genre analysis of the system of patents granted to the University of Toronto in the 1920s for its method for producing insulin and the medical substance itself, the article discusses the changes that Toronto's Insulin Committee was able to make to the patent genre and to a broader system of interrelated genres.This case study confirms that genre innovation and transformation are indeed possible, even in the most formal and stable environments.It also demonstrates that in order to appreciate the strategic goals and rhetorical actions performed by genres, we have to situate them within the specific socio-historical circumstances in which they were enacted.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.230
Teacher spread0.182 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes2
Has abstractyes

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