MétaCan
Menu
← Back to cohort
Record W3209779484 · doi:10.32920/ryerson.14663427.v1

Lovesick : a short documentary film investigating the changing environment and landscape of a small Canadian lake

2021· preprint· en· W3209779484 on OpenAlexaffabout
Lauren Bridle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsCarleton University
Fundersnot available
KeywordsShoreGeographyNatural (archaeology)FishingSpace (punctuation)Balance of natureArchaeologyPolitical scienceEcologyFisheryLaw

Abstract

fetched live from OpenAlex

The short documentary film, Lovesick, explores the changing environment and landscape of a small Canadian lake through the testimonies of the people who live on its shores. Lovesick Lake is one of the smallest bodies of water along the Trent-Severn canal system – a waterway that connects Lake Huron to Lake Ontario. What once was a prosperous region used by Canada’s First Nations people for hunting and fishing, is now a popular location for summer cottages and resorts. Over the last 60 years, shoreline development has increased exponentially while the health of the lake and surrounding land has declined as a result. Now, the lake and local communities face an uncertain future as new vacation developments are being proposed. The film asks: At what cost does Canada’s cottage country come? Lovesick is a response to the materialistic thinking of Canadians and the land that many people take for granted. It aims to enlighten viewers in the hopes that they begin to question the space they occupy and encourage them to respect the delicate balance between nature and humankind. While cottage country is primarily an Ontario lifestyle, Lovesick is a microcosm that aims to shed light on development of natural areas all over North America – and the detrimental effects development can have on the ecosystem.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.093
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.001

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 head0.011
GPT teacher head0.179
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicAmerican Environmental and Regional History→French-language works237,207→