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Record W2592723261 · doi:10.3390/environments4010022

Practice-Based Interdisciplinary Approach and Environmental Research

2017· article· en· W2592723261 on OpenAlexaff
Ranjan Datta

Bibliographic record

VenueEnvironments · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEngineering ethicsManagement scienceEngineering

Abstract

fetched live from OpenAlex

Interdisciplinary researchers and educators, as community members, creators of knowledge, and environmental activists and practitioners, have a responsibility to build a bridge between community practice, academic scholarship, and professional contributions aimed at establishing environmental sustainability. In this paper, I focus on an undervalued area of environmental politics, practices, and often unarticulated assumptions which underlie human–environmental relations. This article challenges interdisciplinary studies that are not connected with practice by reconfiguring the meaning of a community-based, interdisciplinary approach. Drawing from works by Foucault, Latour, and Haraway, this paper first shows how to reconfigure the meaning of an interdisciplinary approach. Second, using Bourdieu and Brightman’s ethnographic studies as a framework, the paper situates practice as central to our efforts to deconstruct and replace current interdisciplinary initiatives with a practice-based approach. Through a practice-based interdisciplinary approach (PIA), environmental educators and researchers gain an awareness of and learn to make an investment in sustainable communities. As teams of environmental researchers practising in the local community, they are meaningfully involved with the community, with each other, and with the environment.

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)0.109
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1090.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.008
Science and technology studies0.0130.087
Scholarly communication0.0200.017
Open science0.0050.019
Research integrity0.0080.009
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 head0.182
GPT teacher head0.498
Teacher spread0.316 · 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 designTheoretical or conceptual
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

Citations9
Published2017
Admission routes1
Has abstractyes

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