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Record W4220958516 · doi:10.1080/00071005.2022.2056575

COVID-19 AND THE INTERNATIONAL BACCALAUREATE: A COMPUTER-ASSISTED DISCOURSE ANALYSIS OF #IBSCANDAL

2022· article· en· W4220958516 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBritish Journal of Educational Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SociologyDiscourse analysisPandemicPedagogyPolitical scienceLinguisticsVirologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

Covid-19 has occasioned ongoing shifts in discourse as language changes to reflect and shape new stages of the global pandemic and different voices weigh in on topics, such as infectious diseases and vaccine efficacy. This study looks at an instance of this that relates to the ‘global education industry’, where cancellation of the International Baccalaureate’s May 2020 high stakes examination instigated a wide-ranging discussion about the organisation. This was triggered by the publication of IB results for 174,355 students in 146 countries, many of which showed large discrepancies between predicted and final grades. Using computer-assisted discourse analysis and a corpus of tweets containing the hashtag #ibscandal, patterns of language use are analysed, providing valuable new insights into the impact on students in different national contexts.

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.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.407
Teacher spread0.363 · 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