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Record W4411958756 · doi:10.59236/td2010vol4iss21239

Shifting Attitudes

2010· article· en· W4411958756 on OpenAlexfundno aff
Susan Nesbit, Alex Mayer

Bibliographic record

VenueTransformative Dialogues Teaching and Learning Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsnot available
FundersHuman Resources and Skills Development CanadaSecretaría de Educación PúblicaMichigan Technological UniversityU.S. Department of Education
KeywordsPsychology

Abstract

fetched live from OpenAlex

This paper reports on the findings of a review of the literature on interactions between affective and cognitive learning, in general, and, specifically, during field trips for university students in natural science or engineering disciplines.The paper presents a study that tests concepts raised from the literature review by exploring affective learning during a 10-day field trip to a water-poor region of Mexico.Student beliefs about the relevance of water resource management are compared before and after the field trip using a survey instrument.Also, student beliefs are compared to those of water resource experts.Results of the study are put in the context of the literature and strategies are recommended for optimizing affective learning within experiential, informal activities such as field trips.

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)0.001
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.004

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 head0.029
GPT teacher head0.347
Teacher spread0.319 · 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
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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