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Record W3096649147 · doi:10.5430/ijhe.v9n9p41

Phenomological Happiness of the History and Geography Teacher in Rural

2020· article· en· W3096649147 on OpenAlexvenueno aff
Yone Vilchez Cisneros, Doris Fuster-Guillén, Roger Pedro Norabuena Figueroa, Reyna Luisa Cruz Shuan

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessPsychologyConsciousnessPhenomenology (philosophy)Interpretation (philosophy)Meaning (existential)EpistemologySocial psychologySociologyPedagogyComputer science

Abstract

fetched live from OpenAlex

The purpose of this research was to describe, analyze and interpret the essence of the experience lived by teachers of the specialties of History and Geography in Huanca Sancos - Ayacucho. Reflective and empirical methods of hermeneutical phenomenology were developed; the latter is responsible for addressing reality based on the subject's consciousness in understanding the meaning of what has been experienced; research was oriented from the qualitative approach and interpretive paradigm. The information was obtained from a sample made up of teachers who narrated their experiences through anecdotes. Close observation and a conversational interview were also used as instruments and techniques. The analysis and interpretation of the information allowed to discover individual and group meanings such as happiness, reflection, satisfaction, and tranquility; thus building as a general thematic unit of this study: the happiness of the teacher, understood as fullness, well-being and satisfaction; it is actually the source of all pedagogical transformation. To conclude, it can be stated that the study has the character of stimulation and satisfaction in the practice of pedagogical happiness as a habit that leads to making thinking more flexible and discovering better options for a change.

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 score0.135
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.346
Teacher spread0.297 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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