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Record W7036180065

Assessing the divide between humans and the natural world: effects of increased experience in natural areas

2017· dissertation· en· W7036180065 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicOrthoptera Research and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionTSG101Gestational periodHyporeflexiaDiafiltrationArticular cartilage damage
DOInot available

Abstract

fetched live from OpenAlex

There is speculation as to whether humans are able to comprehend, appreciate, and protect natural environments when they have received minimal or no exposure to such areas. There are many speculative explanations for this bifurcation between humans and nature; however, there is an absence of a solution to address the issue. Current research strongly emphasizes the health benefits of receiving more exposure to nature, especially since much of North America has witnessed a dramatic shift towards a more technologically driven culture that is heavily reliant on the urban environment.
\nThis study investigates the declining connections between humans and the natural world, and the effect nature-based experience has on individual perspectives regarding stewardship and environmental awareness. Utilizing qualitative research methods, interviews were conducted with nine participants of three different Outward Bound Canada expeditions in order to determine whether a trip of one week or longer had influenced participants? sense of stewardship and/or environmental connectivity.
\nResults demonstrate a positive correlation between participant exposures to isolated natural environments and an increased sense of environmental commitment or stewardship, especially with regard to forming connections with nature, willingness to participate in environmental-based volunteer initiatives, and mitigating fear of the outdoors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.291
Teacher spread0.256 · 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 teacher head, 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
Published2017
Admission routes1
Has abstractyes

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