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A Pilot Study of Small Craft Harbors in Nova Scotia, Canada: Examining Livelihoods Associated with These Facilities

2022· article· en· W6902140630 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLivelihoodGovernment (linguistics)CraftPublic policyFishingState governmentProject commissioning

Abstract

fetched live from OpenAlex

In Canada, small craft harbors (SCHs) are a federal government responsibility under Fisheries and Oceans Canada (DFO). SCHs are economic centers for many rural coastal communities in Canada. By studying the role of SCHs on livelihoods one can delineate their importance to the users and community. The pilot study utilized semi-structured telephone interviews to speak with 19 participants from Nova Scotia, Canada. Participants included users and harbor authorities on whether the SCH they use or manage impacts their livelihood or community. Results indicate SCHs provide an essential avenue for users and communities to support their livelihoods. However, the degree to which they were essential varies between SCHs. SCH users have sufficient capital assets (financial, natural, human, social, and physical) to use the facilities. However, several participants indicated they lack funds from the federal government to maintain their SCHs. Therefore, two critical hindrances in supporting prosperous livelihoods were funding from the federal government and SCHs that lack access to a harbor authority.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.003
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.204
Teacher spread0.146 · 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 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
Published2022
Admission routes2
Has abstractyes

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