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Record W1607541867 · doi:10.7202/1005587ar

Film Festival/Film History: The Impact of Film Festivals on Cinema Historiography. Il cinema ritrovato and beyond1

2011· article· en· W1607541867 on OpenAlexvenueno aff
Francesco Di Chiara, Valentina Carla Re

Bibliographic record

VenueCinémas Revue d études cinématographiques · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicItalian Fascism and Post-war Society
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyMovie theaterFilm studiesConstruct (python library)Visual artsArtAestheticsLiteratureSociologyHistoryComputer scienceArchaeology

Abstract

fetched live from OpenAlex

The aim of this essay is to offer some coordinates for the analysis of the impact of film festivals on cinema historiography. Although film festivals have had a pivotal role in film studies since the 1940s in determining various aspects of research interests, both on a critical and theoretical level, it is only in recent years that they have gathered academic attention as an autonomous research field. Moreover, even among most studies of film festivals, the relationship between film festivals and cinematic historiography seems to have been overlooked. This essay is conceived, therefore, as a provisional contribution, attempting to delineate the scope of the topic and set some parameters for further research. After defining what we mean by the impact of film festivals on cinema historiography, we try to construct a conceptual framework, capable of examining how the strategies deployed by film festivals—such as their structure, schedules, published materials and round tables, as well as their identification of a target audience—implicitly highlight the potential impact of film festivals on cinema historiography. To exemplify our theoretical statements, we refer to different kinds of film festivals and, more particularly, make a few remarks about a very particular case, Il cinema ritrovato in Bologna.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.280
Teacher spread0.234 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations9
Published2011
Admission routes1
Has abstractyes

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