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Record W2503368535 · doi:10.1080/22041451.2016.1214888

How things make things do things with words, or how to pay attention to what things have to say

2016· article· en· W2503368535 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCommunication Research and Practice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsInternet of ThingsAestheticsInternet privacyComputer sciencePsychologySociologyArt

Abstract

fetched live from OpenAlex

While organisational communication research has traditionally limited talk to human beings, a trend within the Montreal School (TMS) of the Communicational Constitution of Organizations (CCO) perspective acknowledges that ‘things do things with words’ as well, and criticises the ‘bifurcation of nature’ into two distinct realms: materiality and discourse. However, due to a preference for studying human discourse, many TMS studies still may give the impression that only human spokespeople can make objects talk. This paper uses data from an ethnographic case study to argue that CCO is well equipped to recognise that other sorts of objects may speak as well, and that they enter the realm of language through yet other objects (i.e. their ‘spokesthings’). In doing so, this paper advances an argument that will counter critiques of TMS scholarship that propose it reduces the role played by objects to their interpretation by humans.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0050.021
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.444
Teacher spread0.326 · 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