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Record W7019407924

GAINING PERSPECTIVE: WHAT DO AFFECTED PERSONS THINK ABOUT OIL SANDS PROCESS-AFFECTED WATER REMEDIATION USING CONSTRUCTED WETLANDS ENHANCED BY GENOMICS?

2023· dissertation· en· W7019407924 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsnot available
FundersCanadian Federation of University Women
KeywordsEnvironmental remediationOil sandsWetlandContext (archaeology)TailingsConfusion
DOInot available

Abstract

fetched live from OpenAlex

Effective oil sands remediation requires integrated and holistic knowledge of the problems and potential solutions that can only be acquired through participation and input from affected persons. Mining of Alberta's oil sands consists of water-intensive processes such as hot water extraction, resulting in large quantities of contaminated oil sands process-affected water (OSPW) stored in tailings ponds that require remediation before release. Hence, as mining projects advance, the need for sound policy and improved methods for remediation and release intensifies. Constructed treatment wetland systems (CTWS) have been identified as a viable remediation option, and genomics can be employed to optimize microbial activity, enhancing remediation efficacy. However, researchers have paid limited attention to the priorities and values held by those affected by OSPW remediation and particularly to CTWS enhanced by genomics. To address this gap, I co-designed and performed this research with affected persons, including Indigenous Peoples, oil and gas industry employees, scientists studying CTWS and genomics, and regulators and policymakers considering OSPW remediation. My sequential mixed-method research used a literature review, media review, and focus groups to examine opinions on CTWS and genomics for OSPW remediation and to build a Q-methodology concourse and Q-set for future research to systematically study participant viewpoints. This study found that affected persons have a wide range of opinions about CTWS and genomics in the context of OSPW remediation. They identified barriers to participating in discussions and decisions about CTWS and genomics, the main ones being confusion around proposed technologies and inadequate communications, which can inform future community engagement processes through recommendations. Some affected persons supported CTWS and genomics when methods were piloted, when they knew CTWS and genomics had been used in other applications, and when they knew research was underway or completed. Conversely, affected persons argued that information about CTWS and genomics is sparse and that there is no evidence that CTWS and genomics will not harm the environment, wildlife, or human health and culture. Novel findings were the conflict between affected persons from regulatory bodies and affected persons in industry around placement and continuity of CTWS after a mine is closed, and affected persons’ assertions that wetlands have a spirit. Cultural Theory’s purpose is to use the typology of five ways of organization to categorize, examine, and critique deliberative quality in engagement with affected persons, noting that the most robust solutions to the most challenging problems emerge when all five solidarities are sought and blended in solution generation. In this study, Cultural Theory’s three active solidarities—individualism, hierarchy, and egalitarianism—were observed, while fatalism and autonomy emerged when some participants considered perspectives others might hold. I witnessed synergies between the solidarities, suggesting a capacity for clumsy solutions that incorporate voices from all concerned; however, solidarities are missing in decision-making around remediation policy for OSPW. More research is needed, particularly with local communities, to capture and explore the perspectives of those significantly affected by OSPW remediation. These affected persons could pursue the goals of maximizing accessibility of information and conversations as well as responsiveness to each other’s concerns to enhance deliberative quality and implement robust solutions.

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)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.006
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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 designQualitative
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
Published2023
Admission routes2
Has abstractyes

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