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Record W3201511369 · doi:10.5210/spir.v2021i0.12057

LEAVING HOME TO COME HOME: REBUILDING JOURNALISTIC GATE-KEEPING ONE FACT AT A TIME

2021· article· en· W3201511369 on OpenAlexaff
Paula Joy Todd

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsGatekeepingJournalismMisinformationBlameSociologyPoliticsMedia studiesPolitical scienceLawPublic relationsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Confusion about modern post-publication fact-checking is dissipating as the practise nears its 20th anniversary in the United States (where it began in earnest), and misinformation settles as a resistant and sometimes deadly drag on reality. But a nuanced analysis of fact-checking’s role in and on the journalistic field (Bourdieu, 1993, 1998; Benson & Neveu, 2010) is troubled by such independent characterizations of post hoc claim verification as: a revolt against journalism; a professional or social reform movement; a new genre; an entrepreneurial exercise; a status-seeking ploy; and/or a psychologically ineffective or damaging reinforcement of falsehoods. Given that accuracy is an ethical requirement of normative journalism and the defining characteristic of ‘news’( hence the political duplicity of the oxymoronic term, ‘fake news’), the birth of independent fact-checking troubles the practice of journalism and, by proxy, political knowledge, and can thus also be read as critique of the field writ large. While gatekeeping (Lewin, 1947) is assumed dead, and gate-watching (Bruns, 2003) on life support, modern fact-checkers are nevertheless culling and privileging information, while simultaneously adjudicating and assigning blame for public speech, which traditional ‘neutral’ journalism avoids. Using both both academic and journalistic qualitative and quantitative interview tools, and framed by field, gatekeeping/watching, and discourse theories (thus emulating the journalistic-academic hybrid model deployed in fact-checking), I examine the largely unexplored area of journalistic re-entrenchment and introduce the reverse-gate-keeping theory of ‘information corralling’ in the era of escalating misinformation.

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)0.003
metaresearch head score (Gemma)0.006
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.071
GPT teacher head0.388
Teacher spread0.317 · 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 designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

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