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Record W4413838382 · doi:10.24908/iqurcp19771

The Construct of Hope

2025· article· en· W4413838382 on OpenAlexaffvenue
Talia Economakis

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2025
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsSR Research (Canada)
Fundersnot available
KeywordsConstruct (python library)EpistemologyComputer sciencePhilosophyProgramming language

Abstract

fetched live from OpenAlex

The research I was involved in mainly focused on the aspects of destructive or toxic types of leadership and their effects on the organization’s performance. The goal of this research was to call attention to such shortfalls in leadership and devise solutions to mitigate organizational downfall. In addition, our research has investigated the construct of hope and how hope can be mobilized to foster positive well-being and by extension, effective leadership. By making hope a foundational guiding tool in the practice of leaders, they will gain agency in their work and create a reality that reflects their goal of positively shaping the future of their followers, instead of becoming derailed by circumstances beyond their control. Our research will call attention to broader measures of success in organizations, namely hope, to illustrate how hope-growing leadership can provide new insights for sustainable efforts to organization effectiveness. Our hope is that this research will benefit stakeholders in various domains, including education, medicine and mental health. It will also explore the concept of hopelessness and its detrimental consequences on mental health and performance. By examining current theoretical frameworks and empirical studies, this review aims to synthesize how hope functions as a protective factor and a developmental asset in personal and professional contexts, while also highlighting the detriments related to its absence.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
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 head0.069
GPT teacher head0.408
Teacher spread0.339 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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