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Record W2791465707 · doi:10.1093/cjres/rsx032

In Memoriam: Susan Christopherson (1947–2016)

2017· article· en· W2791465707 on OpenAlexaboutno aff
Meric S. Gertler, Morgan Thomas, Amy Glasmeier

Bibliographic record

VenueCambridge Journal of Regions Economy and Society · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAffectionPoliticsSociologyValue (mathematics)LawPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Susan was a dear and long-standing friend and colleague. I first met her in the first year of my Master’s studies at Berkeley in 1977, when I enrolled in Allan Pred’s human geography seminar, along with Susan, Michael Storper and a small handful of equally interesting and stimulating colleagues. Even at that very early stage in her academic career, Susan struck me as someone who was unusually poised, experienced and remarkably wise beyond her years. She was also unfailingly warm, open and generous—both intellectually and personally. Perhaps because we shared a northern upbringing—hers in Minnesota, mine in Ontario—we naturally gravitated towards one another. Her Nordic roots and Minnesota perspective seemed to confer upon her a lifelong fascination with—and affection for—all things Canadian. I think she secretly felt that Canada, with its public healthcare and its more fully elaborated welfare state, represented a kinder, gentler place that her own country might one day become. She was fond of saying “Of course, you Canadians have this all figured out” or “We have so much to learn from you Canadians”. In fact, Susan and I shared many other affinities besides our northern roots and affection for all things Canadian. As scholars, we both came to appreciate the value of comparative approaches to social science. Susan had a deeply developed sense of the intellectual gains to be made by comparing social systems in different political economies. And she adopted this as a central device in so much of her work.

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)0.001
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: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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 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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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