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Record W4402672390 · doi:10.46303/jcve.2024.36

At the Cost of Momentum: The Case for Truth-Grounded Activism

2024· article· en· W4402672390 on OpenAlexaff
Daniel John Anderson, Susan T. Gardner

Bibliographic record

VenueJournal of Culture and Values in Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Science and Policy Research
Canadian institutionsCapilano University
Fundersnot available
KeywordsGrounded theoryMomentum (technical analysis)EpistemologyPolitical scienceSociologyEconomicsPhilosophyQualitative researchSocial science

Abstract

fetched live from OpenAlex

In the face of injustice, there is often a strong desire to mobilize others to immediate action. However, building public support is difficult when the issue is complicated. This leaves many activists tempted to present matters in simple, undifferentiated terms, as nuance can dampen momentum. However, oversimplification tends to be at odds with truth and it is this tension, between truth and activism, that is the focus of this paper. We begin by exploring the kind of communication that best mobilizes masses of people and note the inverse relationship between motivational as opposed to truthful communication. We then note that, though propaganda is more efficient in creating momentum, it nonetheless carries inherent dangers in that it may (i) over focus on symptoms rather than the disease; (ii) fuel authoritarian personality-types; and (iii) undermine the lifeblood of democracy. We conclude by suggesting that Philosophy for Children is a welcome educational response to this problem because it focuses on relevant contemporary issues, while fostering thinking skills that has the potential to lead to long lasting change grounded in truth. Ultimately the message is that a society and its citizens will do better by embracing pedagogical interventions aimed at fostering “active thinkers” rather than “activists.”

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.492
Teacher spread0.417 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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