MétaCan
Menu
Back to cohort
Record W4376458608 · doi:10.7202/1098863ar

Revealing Homo Donans: Liberating the Unilateral Gift from Commodity Exchange

2023· article· en· W4376458608 on OpenAlexvenueno aff
Genevieve Vaughan

Bibliographic record

VenueRecherches sémiotiques · 2023
Typearticle
Languageen
FieldNeuroscience
TopicEmbodied and Extended Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsDatabase transactionInterpersonal communicationCommodificationOrder (exchange)CommodityProfit (economics)CapitalismSocial exchange theoryStatus quoPsychologySociologySocial psychologyEconomicsPolitical scienceNeoclassical economicsMarket economyLawPoliticsComputer science

Abstract

fetched live from OpenAlex

The nurturing of the infant from birth onwards provides an example of unilateral gift giving made necessary by the helplessness of the child who cannot exchange an equivalent for what she is given. This material transaction, the giving and receiving of goods and care, creates basic interpersonal schemas of material communication, which underlie verbal communication. They differ from the schemas created by quid pro quo exchange. The market economy is composed of both types of transactions, but unilateral gifts are given to and taken by the exchange transaction mechanism in order to create profit. Reinterpreting maternal care as a free communicative economy points towards a redefinition of the human as Homo Donans and provides a way out of the end of the world scenario to which capitalism has brought us. Using ideas of Marx, Vygotsky, Rossi-Landi, Sohn-Rethel, Lakoff, the findings of recent infancy research, interpersonal neurobiology and modern matriarchal (Goettner-Abendroth) and matricentric (O’Reilly) feminism, I propose a radical shift towards the gift paradigm.

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)0.002
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.019
Scholarly communication0.0020.005
Open science0.0000.003
Research integrity0.0010.002
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 head0.258
GPT teacher head0.363
Teacher spread0.105 · 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
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRecherches sémiotiquesSame topicEmbodied and Extended CognitionFrench-language works237,207