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Responses to Hugh Heclo's On Thinking Institutionally

2010· article· en· W1911414497 on OpenAlexaff
Robert C. Fennell, Richard S. Ascough, Tat‐siong Benny Liew, Michael Lee McLain, Nancy Lynne Westfield

Bibliographic record

VenueTeaching Theology & Religion · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsQueen's UniversityAtlantic School of Theology
Fundersnot available
KeywordsConversationOrder (exchange)SociologyRace (biology)Energy (signal processing)Media studiesEpistemologyGender studiesPhilosophy

Abstract

fetched live from OpenAlex

Hugh Heclo's recent book On Thinking Institutionally (Paradigm Publishers, 2008) analyzes changes that have taken place in the past half century in how North Americans tend to think and act in institutions. The volume is receiving particular attention as it can be applied to higher education and to religious denominations, and so deserves consideration by those who teach in theology and religious studies. At an October 2009 conference, The Wabash Center hosted a lively discussion of Heclo's volume among invited religion and theology scholars, which resulted in the present compilation of four short responses to the book. What was and is clear from these responses is that while Heclo has identified a crucial issue, his analysis and prescription leave important theoretical and practical questions untouched. Indeed part of the energy around the discussion of the book flowed from the ways in which his lack of attention to social class, gender, race, and age circumscribed his ability to robustly describe and diagnose the challenge that gave rise to his book. In order to orient readers to the volume and discussion of it, the “Conversation” begins with a descriptive review of the book.

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)0.007
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0100.013
Scholarly communication0.0070.008
Open science0.0020.004
Research integrity0.0160.023
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.328
Teacher spread0.311 · 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
GenreCommentary

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

Citations3
Published2010
Admission routes1
Has abstractyes

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