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Record W4205097198 · doi:10.18778/1733-8077.13.4.03

Considering Essays: The Social Construction of Subcultural Value

2017· article· en· W4205097198 on OpenAlexaff
Scott Grills

Bibliographic record

VenueQualitative Sociology Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsBrandon University
Fundersnot available
KeywordsSociologyMeaning (existential)Value (mathematics)SocializationCurrencyEpistemologyAction (physics)sortSet (abstract data type)Rank (graph theory)Everyday lifeSocial scienceSocial psychologyPsychologyLinguisticsComputer science

Abstract

fetched live from OpenAlex

The article examines the social processes that accompany the social construction of value within subcultural settings. Taking the evaluation of university essays as the case-at-hand, this paper argues for the importance of attending to the generic social process of assigning evaluative meaning. Specifically, this article locates these processes relative to the themes of: 1) socialization of new academics, 2) contextualizing the essay pedagogically and pragmatically, 3) grades as currency, 4) recipes of action and meaning-making, 5) assigning grades, and 6) managing troublesome cases. The collective work that we do to rank, sort, evaluate, and determine the relative worth of social objects reflects a set of processes that are to be found in multiple settings. This article contributes to our understanding of these rather central everyday life activities.

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)0.023
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.005
Science and technology studies0.0140.049
Scholarly communication0.0250.012
Open science0.0020.016
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.166
GPT teacher head0.453
Teacher spread0.287 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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