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Record W7127151056 · doi:10.18357/wg22201642

Trash Talk in Edmonton

2016· article· W7127151056 on OpenAlexaboutno aff
Marie-Josée Valiquette

Bibliographic record

VenueWestern Geography · 2016
Typearticle
Language
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsGarbageEnvironmental justiceDumpingNatural (archaeology)Human healthWaste disposalMunicipal solid wasteUrban planning

Abstract

fetched live from OpenAlex

Edmonton possesses the largest interconnected parkland in North America. Unlike other popular North American parks, there is little scholarly research on the history of this environmental asset. This paper highlights preliminary research towards a module in a historical digital atlas about Edmonton’s River Valley – a module that provides insight into waste mitigation and the management of local natural resources, where an urban problem can result in desirable amenities: such as, an urban green space. During the City of Edmonton’s formative years, locations for dumping garbage were chosen solely for inexpensive cost and convenience; impacts on the environment and human health were not concerns. Throughout the twentieth century, environmental regulations evolved to account for a myriad of issues that occur from accumulating waste – for instance, leachate and methane containment. By the 1960s, the broader environmental movement coalesced into a reaction against unnecessary pollution, but most importantly, it emphasized human health and strengthening our relationship with our environment. Through interviews with City of Edmonton Waste Management employees and an analysis of historical documents, various scholarly works, and contemporary local, and provincial,legislation, this article discusses the challenges and implications of transitioning landfills to parks in Edmonton within different eras.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes1
Has abstractyes

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