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Record W3212294359 · doi:10.1080/15512169.2021.1987259

What Do People Want from Politics? Rediscovering and Repurposing the “Maslow Hierarchy” to Teach Political Needs

2021· article· en· W3212294359 on OpenAlexaff
Patrice Dutil

Bibliographic record

VenueJournal of Political Science Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaslow's hierarchy of needsHierarchyPoliticsAdaptation (eye)SociologyDemocracyEpistemologyPublic relationsPolitical sciencePsychologySocial psychologyLaw

Abstract

fetched live from OpenAlex

This article presents a memorable method to introduce new students to the concept of political needs. Using an adaptation of Abraham Maslow’s “Hierarchy of Needs” to one of “Hierarchy of Political Needs” the instructor presents how needs for “physiological survival,” safety, belonging, “recognition,” and “democratic participation” have shaped political motivations as well as state and partisan responses. The article discusses Maslow’s original arguments and the criticisms that have been leveled against them. It also shows how the original hierarchy has been adapted by scholars since the 1950s. It then demonstrates how a theoretical framework on political needs can be created and shows how the concerns about the Maslow Hierarchy can be used to trigger student discussion. The article finally presents how the concepts are presented through lectures, self-reflection activities, and discussion.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.005
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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 designTheoretical or conceptual
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

Citations6
Published2021
Admission routes1
Has abstractyes

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