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Record W4224274620 · doi:10.32920/ifmj.v2i1.1511

Collaborative, Crowd-sourced and Interactive Documentary

2022· article· en· W4224274620 on OpenAlexvenueno aff
Susan Cardillo

Bibliographic record

VenueInteractive Film and Media Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)RebootFeelingNarrativeSocial mediaSociologyPublic relationsPsychologyComputer scienceWorld Wide WebPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Campus Reboot is a crowd-sourced, interactive and collaborative web-based documentary project and course. The project is a living document of the historical times of college during a pandemic and aftermath. This project was created during COVID-19 to give college students a way to express their feelings and create interactive video projects. Campus Reboot began as experimental research and a need to find ways for students to be creative during a pandemic. Practice-based research is an original investigation undertaken to gain new knowledge based on the practice and outcomes of that practice. There were three areas of involvement that were analyzed: the perspective of the students, the perspective of other instructors, and the perspective of those of us putting it all together. Working with fifteen colleges around the world, students created videos, based on prompts, to share their feelings about college during COVID-19. Campus Reboot allowed students to not only create works that speak to the voice of their generation, in the midst of a historical pandemic but also to work with the footage from other schools to create a broader story of our times. This project and paper look at collaborative, crowd-sourced, and interactive documentary as tools for new narratives, social engagement, and a combined voice of a generation during a global crisis. This type of collaboration can be re-worked to fit different types of projects. With minimal cost and the use of multi-media, college students can collaborate internationally to tell stories of their generation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.287
Teacher spread0.279 · 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 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
Published2022
Admission routes1
Has abstractyes

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