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Record W7066500870

Il museo elettronico. Un seminario con Marshall McLuhan

2018· book· it· W7066500870 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS Research product catalog (Sapienza University of Rome) · 2018
Typebook
Languageit
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodTSG101HyporeflexiaFusible alloy
DOInot available

Abstract

fetched live from OpenAlex

Il 9 e 10 ottobre 1967, al Museum of the City di New York. Marshall McLuhan e Harley Parker discutevano sul senso dei musei nell’epoca elettronica e sulle possibilità di un radicale cambio di strategia negli allestimenti, insieme a un nutrito gruppo di esperti e addetti ai lavori degli Stati Uniti e del Canada. La fama di McLuhan era al culmine in quell’anno. Ma qual era stata l’evoluzione delle idee e delle pratiche dei musei dagli anni Trenta ai Sessanta? E come interpretare le evidenti relazioni tra le idee del padre fondatore della scienza dei media e il clima delle avanguardie – americane e non solo – dei Sixties, il decennio-origine di tutta la cultura successiva? Ed è possibile il recupero delle posizioni di McLuhan nel dibattito attuale sulle tecnologie e la comunicazione dell’heritage? Il saggio di Donatella Capaldi affronta questi problemi e ricostruisce le relazioni fra il seminario e l’evoluzione della mediologia nel suo momento esplosivo. Il testo del seminario – tradotto e curato dall’autrice -, inedito in inglese e in italiano e di per sé interessante e ancora attuale, è pubblicato nella sezione finale del volume.

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 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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.009
Scholarly communication0.0000.001
Open science0.0060.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.069
GPT teacher head0.337
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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