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Record W1566270068 · doi:10.32920/ryerson.14655954.v1

"Sustainable but just on the edge" : assessing the sustainability of the commercial whale-watching industry in the Lower Bay of Fundy, New Brunswick, Canada

2021· preprint· en· W1566270068 on OpenAlexaffabout
Eli G Bamfo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBaySustainabilityWhaleTourismVulnerability (computing)BusinessMarketingFisheryEnvironmental resource managementGeographyEcologyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

The principal contention of this study is that within the sustainability paradigm, there are factors which can be characterized as agents of strength (factors with a reinforcing effect on sustainability) or vulnerability (factors that inhibit sustainability or are indicators of a non-sustainable system). This paper aimed to ascertain the agents of strength and vulnerability within the commercial whale-watching industry in the lower Bay of Fundy, New Brunswick. A research framework was developed based on the literature on the whale-watchng and wildlife-based tourism. The framework was used to assess the management, environmental and economic sustainability dimensions of the whale-watching industry in the aforementioned region based on data gathered from personal interviews with tour operators and self-administered questionnaires from the whale-watching customers. Several factors were found to be positively reinforcing the industry, including: the consistency of the whale encounters and the high level of customer satisfaction. On the other hand, a number of variables were also identified as potential areas of vulnerability, such as: the present downturn in tourist visitation to the region and the need for an improved, consolidated marketing program.

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 score0.019
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.057
GPT teacher head0.375
Teacher spread0.318 · 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
Published2021
Admission routes2
Has abstractyes

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