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Record W3121939167 · doi:10.14288/ce.v12i2.186587

Disaster Capitalism, Rampant EdTech Opportunism, and the Advancement of Online Learning in the Era of COVID19

2020· article· en· W3121939167 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOpen Collections · 2020
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCapitalismOpportunismNeoliberalism (international relations)Public relationsPandemicPublic healthPolitical scienceSociologyEconomic growthCoronavirus disease 2019 (COVID-19)Political economyPublic administrationEconomicsMedicineLaw

Abstract

fetched live from OpenAlex

The authors consider the ways in which educational responses to COVID19 exemplify opportunistic disaster capitalism. Prior to the pandemic, neoliberal influence increasingly impacted education systems all over the world, pushing for increased privatization in/of schools. COVID19 has created conditions for private technology companies to push for increased participation in public schools. That is, corporations are using this health crisis to further mobilize the neoliberal agenda, and encourage policies, practices, and technological infrastructure that will be used to rationalize ongoing online learning. In turn, we ask: What are the motivations and implications of inviting private EdTech into public education? How does EdTech encourage a move to online learning; c) what are the overall impacts of online learning? Under the veil of the panic of a global health crisis, our public education systems in Canada are being put at risk.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.317

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 communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.296
Teacher spread0.260 · 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