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Record W2044762252 · doi:10.4141/cjps07116

Who’s invading what? Systems thinking about invasive species

2007· article· en· W2044762252 on OpenAlexvenueno aff
Brendon M. H. Larson

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsInvasive speciesBiodiversityEcologyEnvironmental ethicsIntroduced speciesPopulationGlobal warmingBiologyClimate changeSociology

Abstract

fetched live from OpenAlex

While invasive plant species have dramatic and varied effects, this paper examines the focus of this symposium on their “threat to native biodiversity and ecosystems”. This claim implies that there is (i) an enduring something, (ii) it is native, and (iii) it is under threat from invasive species. I examine these implications in turn, first considering the role of the observer in invasion biology, particularly in preferring a nature characterized by stability rather than flux. Second, I examine the concept of “native” given that humans are thoroughly embedded within natural ecological systems. Third, I demonstrate how our exclusion of humans conditions us to consider invasive species a primary threat rather than one among many interacting causal agents of global change; in particular, recent evidence indicates that these agents, which include human-caused disturbances and global warming (not to mention human population growth and global trade), may overwhelm the effects of invasive species per se. For these and other reasons, some ecologists have argued that ecological change is inevitable and that our concerns about invasive species are unjustified. I discuss these issues and suggest ways for ecologists to conduct engaged research through appropriate advocacy and engagement with stakeholders dealing with local invasive species. Key words: Advocacy, culture, philosophy of nature, sociology of science

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.211
Teacher spread0.194 · 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

Citations34
Published2007
Admission routes1
Has abstractyes

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