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Record W2994118126 · doi:10.1353/llt.2019.0037

Contemporary Challenges Teaching Labour History

2019· article· en· W2994118126 on OpenAlexvenueno aff
Mark Leier, John‐Henry Harter, Dale M. McCartney, Andrea Samoil

Bibliographic record

VenueLabour / Le Travail · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPaceSyllabusProcess (computing)Work (physics)PedagogyPublic relationsSociologyPolitical sciencePsychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

thinking about teaching as a way to help people develop their own capacities for understanding and for action.It starts not with content and assessment but with the people we are teaching.It pays less attention to facts and ideas, although clearly these remain important, and more to the dynamics of the people in the class, so we may learn from each other and teach each other.It is designed to help us examine new ideas through our experiences and the experiences of others so we may become more effective.The role of the instructor is less to deliver and test for content and more to create a place where people participate and gain confidence in their own power so they push and probe one another and themselves.Teaching in this way is not new.It is a staple of labour education where unions want to have active, militant members.It is sometimes called the "organizing model" of teaching, for it draws upon lessons from effective organizers.It starts not with the mastery of the expert but with the knowledge that people have ideas and experiences they will draw upon and that will shape how they handle new ideas.That in turn pushes the instructor to take seriously not just the content but the people in the group and to work as part of the group.It assumes that how material is delivered matters at least as much as the material itself, for the point is to help people learn how to build oppositional cultures and structures.That is hard to do with a Scantron exam and predetermined learning outcomes.The four essays that follow share some of our experiences and experiments in teaching in a more democratic and participatory way.We are not experts, but we are long-term instructors, educators, participants, and scholars of labour history and labour studies who think about, research, collaborate, and practise teaching against the grain of neoliberalism.Our hope is that readers can adopt, adapt, reject, reshape, and build on our teaching experiences.In this way, we can share and shape our educational vision and our daily practice for a democratic, participatory future.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.049
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0180.046
Scholarly communication0.0160.012
Open science0.0020.009
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0370.003

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 head0.057
GPT teacher head0.308
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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