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Record W2901307373 · doi:10.3917/mana.212.0705

From boat to bags: The role of material chronotopes in adaptive sensemaking

2018· article· en· W2901307373 on OpenAlexaff
Geneviève Musca Neukirch, Linda Rouleau, Caroline Mellet, Frédérique Sitri, Sarah De Vogüé

Bibliographic record

VenueM n gement · 2018
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsSensemakingMateriality (auditing)SociologyContext (archaeology)Cognitive reframingAestheticsStructuringEpistemologyMeaning (existential)EthnographyHistoryAnthropologyPsychologyArtSocial psychologyArchaeologyPolitical sciencePhilosophyPublic relationsLaw

Abstract

fetched live from OpenAlex

In following a material turn in communications, this paper explores how adaptive sensemaking in an extreme context is materially framed and reframed through both time and space. By drawing upon an ethnographic study of the Darwin Expedition, the paper examines in fine-grained detail what Weick (1993) would call a "cosmology episode": during Days 9 and 10 of this expedition, climbers felt that their universe was no longer rational or ordered. A discursive analysis reveals that the "boat" and "bags" had become two central "material chronotopes", through which meaning-making was being collectively reframed once the sense had collapsed. This work assesses the accounts surrounding both objects and moreover explains their roles in prompting the expedition team to reframe core meanings and enact a radical shift in sensemaking. The conclusion discusses the contribution of chronotopes in frame-shifting and the importance of focusing on the central objects structuring the collective sensemaking process in order to yield a better understanding of the role of materiality in an extreme context.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.032
Scholarly communication0.0100.015
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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 designQualitative
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

Citations10
Published2018
Admission routes1
Has abstractyes

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