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Record W2970742825 · doi:10.11575/prism/36888

The Great Collapse: How Afraid Should We Be?

2019· article· en· W2970742825 on OpenAlexaboutno aff
R. M. Fisher

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyHistory

Abstract

fetched live from OpenAlex

Dr. R. Michael Fisher, educator-fearologist, founder and teacher of the School of Sacred Warriorship in the early-to mid-1990s, and many other ventures, talks about the current state of cascading "collapse" of the economic, environmental and social spheres, with implications for the psychic collapse that's also inevitable as extreme conditions of fragility affect everyone, more or less, and sooner or later. He provides several important theories and experiences of how to better understand crises, how to encourage great courage and more importantly fearlessness...and really it is not a very useful question to ask: "How Afraid Should We Be?" Although, that question is a beginning probe into better understanding the path he promotes. This video is a companion to "The Great Citizen" video recently made by Dr. Fisher, so watching both together gives a better balance to his views on the political sphere, and both videos shape a picture of how he is entering politics for the next 10 years (at least)--of which community-building is a core part of his agenda. The Great Citizen: https://www.youtube.com/watch?v=PE7jq... He has lots of experience in community, in diverse communities from the human potential, organic alternative, wellness, therapeutic to liberation communities. He understands the depths of both compassion and wisdom philosophically and through personal experiences. Currently he is teaching his L.E.T. program in the everyday communities of Calgary, AB as a pilot project to engage citizens to take charge of their own lives and the future in a much better way. He proposes in this video on the "Great Collapse" a new type of labor force, to deeply adapt to the challenging and terrifying world we are heading into--coming to face the edge of the cliff. What choices and strategies will we choose?

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0130.011
Scholarly communication0.0090.015
Open science0.0010.005
Research integrity0.0100.021
Insufficient payload (model declined to judge)0.0130.007

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 head0.062
GPT teacher head0.318
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2019
Admission routes1
Has abstractyes

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