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Record W4388109645 · doi:10.54254/2753-7064/8/20230975

The Application of Clip in Short Videos - Take 5 Short Videos as an Example

2023· article· en· W4388109645 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications in Humanities Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNarrativeShadow (psychology)AffectionLonelinessWhite (mutation)Visual artsArtAestheticsPsychologyLiteraturePsychoanalysisSocial psychology

Abstract

fetched live from OpenAlex

The article describes five different short films and their editing techniques. Film 1 focuses on appearance anxiety of young women caused by external influences. The authors use black and white filming and sound montage to add a humorous effect. Film 2 shows the shadow of desire through a transfer student who turns to the dark side and uses flashbacks and j-cuts to add depth and intrigue to the story. Film 3 tells the story of a college student who receives an “F” on her transcript and uses flashbacks and internal monologues to reveal the cause and effect of the story. Film 4 is about a little girl’s day in high school and uses long shots, jump cuts, montage, and empty scenes to tell a story of loneliness and the importance of facing life. Film 5 is a story about family affection and uses continuous editing and montage to create an immersive cinematic narrative.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.002
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 head0.681
GPT teacher head0.627
Teacher spread0.054 · 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