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Record W2884858597 · doi:10.15402/esj.v4i1.316

Humanities for Humanity

2018· article· en· W2884858597 on OpenAlexvenueno aff
John Paul Duncan

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsHumanityBureaucracySociologyPoliticsSocialismService-learningPedagogyHumanitiesPolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

Since 2007, the Humanities for Humanity (“H4H”) course has brought together student experience beyond the classroom, educational experiences for community members who could not otherwise attend university, discussion of social justice, and studies in the humanities. By discussing a selection of rich and influential primary texts from the humanities, course members are introduced to a rudimentary history of the present, focussing on who we have become as members of a concrete social and political reality intersected by capitalism, bureaucracy, liberalism, socialism, anti-essentialism, and post-colonialism. Both the texts and the student-participant encounters are rich, and the sessions are guided by two central classical ideals: the activity of learning is primarily an end in itself, and the most important thing to learn may be who we are. The core course content of H4H is outlined, and the ways in which H4H connects student mentors and community participants are discussed. Implications are drawn regarding what makes H4H a unique form of community service-learning in which service is virtually eclipsed by learning in a process that subverts barriers between people.

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)0.903
metaresearch head score (Gemma)0.662
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9030.662
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.8550.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0000.690
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 head0.310
GPT teacher head0.452
Teacher spread0.142 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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