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Record W3014212369 · doi:10.5325/jpoststud.3.2.0181

Shoot!? Describing the Hand-Cranked Camera and Filmmaking Practices from a Posthumanist Perspective

2019· article· en· W3014212369 on OpenAlexaff
Paolo Saporito

Bibliographic record

VenueJournal of Posthuman Studies · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPosthumanAffordanceAnthropocentrismPerformative utteranceFilmmakingPerspective (graphical)PosthumanismSituatedSociologyConstitutionMovie theaterAestheticsEpistemologyIconicityPsychologyArtPhilosophyEnvironmental ethicsVisual artsComputer sciencePolitical scienceLinguisticsCognitive psychology

Abstract

fetched live from OpenAlex

ABSTRACT Posthumanist approaches to cinema and film studies have so far focused on the posthuman theoretical potential of film content, rather than on cinematic form and filmmaking practices. This article develops a posthumanist approach to these practices by focusing on a case study: the interaction between the hand-cranked camera and the human operator in the first decades of the twentieth century. Drawing on Karen Barad’s theory of agential realism and Jane Bennett’s definition of vibrant matter, I argue that this interaction constitutes a manifestation of material-discursive processes that decenter the anthropos and enact performative entanglements between human and nonhuman agencies. Early descriptions of the technological affordances of the hand-cranked camera, informed by anthropocentric and humanist perspectives, reduce the material constitution of this instrument to its role in the discursive practices of the cinema industry. This article deconstructs these accounts from a posthumanist perspective and develops situated epistemic and ethical reflections that stem from a material-discursive description of the nonhuman instrument and its interaction with the human operator.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.105
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.150
GPT teacher head0.306
Teacher spread0.156 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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