MétaCan
Menu
Back to cohort
Record W4281480073 · doi:10.32920/ifmj.v2i2.1549

What can listening do?

2022· article· en· W4281480073 on OpenAlexvenueno aff
Kim Munro

Bibliographic record

VenueInteractive Film and Media Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningSociologyCognitive reframingNarrativeScholarshipValue (mathematics)Media studiesInformational listeningPoliticsAestheticsEpistemologyPsychologySocial psychologyPolitical scienceCommunicationArtComputer scienceLiterature

Abstract

fetched live from OpenAlex

Documentary has traditionally been used and understood as a communicative tool to frame and impart knowledge about a subject matter. For Paula Rabinowitz, documentary’s “purpose is to speak and confer value on the objects it speaks about” (1994). But what other functions might documentary have if it were to draw attention to how we can also listen? Over the past twenty-five year, listening scholarship has focused on a number of domains including the public sphere (Lacey 2013), participatory democracy (Bickford 1996; Couldry 2010), and media practices (Dreher 2009). Yet, while ‘voice’ as authorship and social participation has been well theorised in documentary, there has been little scholarly attention given to how audiences listen, and what strategies creators can use to evoke different ways of listening. The possibilities for listening as an active role in documentary have further been enhanced through interactive technologies. In this presentation, I draw on my current research and practice around the possibilities for interactive and expanded documentary to promote listening as a way to attend to ethical and political questions. Listening is not a singular action, rather, it can fulfill multiple functions. I propose that practices of listening can reframe the self as collective, reveal multiple co-existing narratives, counter erasure, promote engagement with difference and destabilise a anthropocentric perspective. Drawing on frameworks which include political, non-representational and sound theory, I ground my propositions through a range of interactive and immersive documentaries from the past ten years, each of which attempts to implicate the audience in new and responsible relationships to the historical and phenomenological world.

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 score0.488
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 communication0.0010.001
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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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

Explore more

Same venueInteractive Film and Media JournalSame topicRadio, Podcasts, and Digital MediaFrench-language works237,207