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Record W2747955194

‘Science’- of value to hazard & disaster challenges; an exposé on interdisciplinary research

2016· article· en· W2747955194 on OpenAlexaboutno aff
Elizabeth A. Edwards, Richard M. Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Vulnerability (computing)Futures contractDisciplineParticipatory action researchEngineering ethicsWork (physics)Citizen journalismSociologyHazardPsychological resilienceValue (mathematics)Political scienceEnvironmental ethicsPublic relationsSocial scienceGeographyBusinessEngineeringPsychologySocial psychologyComputer scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

In the Indian Himalaya, ongoing, multi-disciplinary, tri-lateral (Canada, India, UK), participatory (light) research is exploring interconnected questions of mountain system process-hazard dynamics, community heritage-vulnerability- resilience conditions, and rapid development implications (e.g. HEP) in the context of international sustainable development and disaster risk reduction policy accords, in order to fashion a more sustainable future. Pursuing this hybridisation of themes has required large shifts across traditional intra/inter subject boundaries, and so many lessons have already been learned. This approach has without doubt been hugely beneficial in delivering an enhanced level of knowledge involving a range of non-academic organisations and communities speaking their local languages. Here we draw upon these research experiences, making the case that physical sciences are very much part of the effort to seek better understanding of and futures for the world, where a holistic understanding has to draw upon the in-depth approaches and outcomes of a range of disciplines. We seek to show the benefits of this approach to date, but equally to generate debate around a number of contentions; a challenge we should embrace together: (1) Current thinking suggests that Interdisciplinarity has great potential to provide something greater than the sum of its parts. We suggest that this needs to be a genuine meeting of minds and a willingness to work together on the part of all the players. Hence we ask ‘interdisciplinarity for whom and using who’s methods?’ (2) Is this interdisciplinary endeavour merely following an in-vogue paradigm and being used as a vehicle of power for some parts of the interdisciplinary whole? Indeed disciplinarity seems to be negatively cast, being touted as a dirty word by some. But let’s not forget if we don’t develop discipline based knowledge, with parity, then we fail to populate cross-disciplinary endeavours with an improving knowledge and technique baseline, this will in due course stifle the progress of interdisciplinarity. Hence lets not throw out the baby with the bathwater! (3) Employers delivering technical services do still seek depth of disciplinary knowledge, so we need to create degree programmes that deliver societally relevant depth and breadth- this calls for a mix of the old and the new, not just radicalism! (4) The language and approach of interdisciplinarity to date generates genuine discomfort in the scientific community- this we cannot ignore; together, we need to co-produce a better model of interdisciplinarity that fully embraces all of the disciplines represented in any project partnership. These are not mindlessly provocative, anachronistic denials of the world; but instead genuinely open reflections of earth scientists/ geographers who do work in an already diverse discipline and more so across boundaries. We all have to talk, in common tongue, seeking to understand the array of contributions that can be brought into the mix, otherwise interdisciplinarity will develop within intrinsic bias/ reduced capacity and therefore fall short in optimising pathways to impact. This was a Dec 2016 presentation, which we re-discuss with a different multi-disciplinary audience

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)0.026
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.007
Science and technology studies0.0160.123
Scholarly communication0.0240.029
Open science0.0030.019
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0070.001

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 head0.374
GPT teacher head0.561
Teacher spread0.186 · 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.

Study designQualitative
DomainMethods
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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