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Record W2608993414 · doi:10.1108/gm-04-2015-0040

Deconstructing Higgins: gender bias in the Pygmalion phenomenon

2017· article· en· W2608993414 on OpenAlexaff
Len Karakowsky, Nadia C. DeGama, Kenneth McBey

Bibliographic record

VenueGender in Management An International Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsYork University
Fundersnot available
KeywordsPhenomenonPerceptionPsychologyFacilitationSocial psychologyEmpirical researchPower (physics)Conceptual frameworkFace (sociological concept)SociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

Purpose Despite considerable empirical evidence to support the existence of the Pygmalion effect, studies that focus on the role of gender within this phenomenon have produced varied results. Whereas the research has consistently demonstrated the Pygmalion effect with male leaders, less research evidence exists to support this phenomenon among female leaders. This paper aims to present a conceptual framework for understanding the potential role gender can play in the Pygmalion effect and why women may face obstacles in their efforts to serve as Pygmalion leaders. Research propositions are presented with the intent of guiding future research in this area. Design/methodology/approach For the purpose of this theoretical paper, elements of the sociological, structural and psychological perspectives are drawn to present an understanding of the potential influence a leader’s gender has on the facilitation of the Pygmalion effect. A brief background to the Pygmalion effect is first provided followed by research related to this phenomenon. Then the underlying elements of the Pygmalion effect are revisited and attention is drawn to those factors affecting subordinate receptiveness to leader influence. Following that, these ideas are integrated with gender-based research to present the theoretical framework and research propositions. Findings This paper’s framework offers a fuller understanding of the role of a leader’s gender in facilitating the Pygmalion effect by identifying factors that can influence subordinate perceptions of leader efforts. Gender-based cues influence subordinate perceptions of leader expertise and power. In turn, this will influence subordinate receptiveness to leader elicited expectations and supportive behaviors. Practical implications The theoretical framework of this paper suggests that under certain conditions, gender differences can impact subordinate receptiveness to leader behaviors and performance expectations. Managers need to be aware of the gendered-nature of the work context and how it can influence subordinate perceptions of leaders. Otherwise, gender-based cues can unwittingly undermine women who endeavor to elevate follower self-efficacy beliefs. Social implications Stereotypes regarding women in managerial roles can undermine the capacity of women to effectively mentor and inspire others. These biases must be explicitly confronted and challenged. Moreover, given the global nature of the workforce it is critical to understand how national cultures can differ in their attitudes toward women in management in ways that can impede the ability of a woman to facilitate this transformative process. Originality/value The theoretical framework of this paper broadens the understanding of gender differences in the Pygmalion effect. This paper attempts to explain the lack of consistent findings for women who attempted to serve as Pygmalion leaders. It is asserted that rather than a leader’s gender, it is gender-based contextual cues that influence the leader’s capacity to trigger the Pygmalion effect. Thus far, no systematic effort has been made to identify the conditions under which subordinates are receptive to the influence of Pygmalion male or female leader expectations and behaviors.

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)0.042
metaresearch head score (Gemma)0.115
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.042
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.115
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.035
Scholarly communication0.0040.007
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.381
GPT teacher head0.406
Teacher spread0.025 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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