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Record W1976560065 · doi:10.1177/0007650307306638

Frames and Filters

2007· article· en· W1976560065 on OpenAlexaff
Robbin Derry, Sachin Waikar

Bibliographic record

VenueBusiness & Society · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsDistrustLegitimacyFraming (construction)Public relationsHonestyBattleAdversarySociologyPolitical scienceLaw and economicsPoliticsLawComputer securityEngineering

Abstract

fetched live from OpenAlex

Despite growing interest in the dynamics and influences of activist groups, few studies have examined the specific tactics used by activists to achieve legitimacy and how these actions affect target firms or industries. This article studies the history and current state of the battle between tobacco control groups and Big Tobacco in search of evidence for their use of framing—a process of generating shared meaning and purpose through the creation of overarching messages—as a vehicle for carrying out their mission, achieving legitimacy, and thwarting the efforts of adversaries. The authors propose that both sides marshal specific core frames in service of broader master frames, namely the projection of honesty and trustworthiness for the tobacco industry, countered by public health's master frame of distrust of the industry. The evolution of this battle may also be understood within the framework of a two-factor model of trust and distrust; the authors assert that the relationship between tobacco control and the industry will likely continue as one of low trust/high distrust, in part because the master frame of distrust has served multiple purposes for public health activists, including the establishment of greater legitimacy with the public and, by proxy, with the target industry. Several specific conclusions are drawn regarding the functions of distrust and the relationship between framing, trust, and legitimacy.

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)0.000
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: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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