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Record W2072459589 · doi:10.3138/sem.50.2.142

Unravelling the “Ponzi Scheme”? A Different Approach to Graduate Education in German Studies

2014· article· en· W2072459589 on OpenAlexaffvenueabout
Michael Boehringer

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Science Research and Education
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGermanScheme (mathematics)Political scienceMathematics educationPsychologyMathematicsPhilosophy

Abstract

fetched live from OpenAlex

Graduate Studies in German: A Terrible Idea? The rhetoric of crisis has been common currency in discussions on graduate studies in the humanities for as long as I can remember. And there is no question that, then and now, our graduate programs in German churn out far more PhDs than there are tenure-track or even limited-term positions at Canadian universities. Add to this picture the fierce competition for faculty positions from PhD holders from the United States and Germany, and it cannot surprise us that many commentators see graduate school in the humanities as a pyramid scheme perpetuated by faculty members and administrators alike, in the interest of keeping their own classes populated and retaining cheap labour for lowly undergraduate courses. Should we follow the example set by Thomas H. Benton (the pseudonym of William Pannapacker, himself a tenured associate professor) in advising prospective graduate students: “Just don’t go”? Is getting a PhD in German a “terrible idea because the full-time, tenure-track literature professorship is extinct,” as Rebecca Schuman writes in Slate magazine? Are we tenure-track or tenured professors abdicating our responsibilities as teachers? Are we indeed promoting “the self-interest of faculty members at the expense of students” as Mark Taylor would have it, if we don’t shut down our graduate programs or, at the very least, reduce enrolment to a trickle? Well, yes – and no.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.164
GPT teacher head0.486
Teacher spread0.323 · 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
Published2014
Admission routes3
Has abstractyes

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