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Record W3101504112

Interactive and multimedia journalism: evaluating Canadian news media’s implementation of non-traditional storytelling elements in online features about the “Airbnb Effect”

2020· dissertation· en· W3101504112 on OpenAlexaboutno aff
Cecilia Marangon

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2020
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsRentingInteractive mediaMultimediaGlobeStorytellingJournalismComputer scienceDigital mediaNews mediaAdaptation (eye)AdvertisingWorld Wide WebBusinessPolitical scienceArtNarrativePsychology
DOInot available

Abstract

fetched live from OpenAlex

Since spring 2019, numerous Canadian news outlets have reported on the problem caused by short-term rentals in cities across the country and the so-called "Airbnb effect"”— the transferral of apartments from long-term rental markets toward the short-term market via Airbnb and similar platforms. The three examples analyzed integrated multimedia and interactive elements into their online long-forms, which is an important step towards a more beneficial integration, for both journalists and readers, of interactive and multimedia into news reporting. This essay analyzes three examples of multimedia and/or interactive journalism that apply “snowfalling” techniques to news stories about short-term rental issues in Canada, from three different news outlets: The Globe and Mail, GlobalNews and CBC/Radio Canada. This study concludes that while it is clearly important for news outlets to explore new, alternative forms of multimedia and interactive storytelling in a continuously evolving digital news environment, they also need to balance it with a similar effort in reconnecting with the communities that they serve.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.313
Teacher spread0.271 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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