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Record W7135159154

Travailler dans une équipe intergénérationnelle, un parcours semé de défis

2025· other· fr· W7135159154 on OpenAlexaboutno aff
Inece Dolvine Mafogne Kankeu

Bibliographic record

VenueSémaphore (Université du Québec à Rimouski) · 2025
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSocial impactContext (archaeology)Face (sociological concept)
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ : Ce mémoire explore les difficultés rencontrées lors du travail au sein d'équipes intergénérationnelles, au Québec. Pour ce faire, nous avons eu recours à la méthode de l'autopraxéographie, méthode qualitative et introspective. Cette démarche vise à examiner sa propre expérience professionnelle pour générer des connaissances basées sur cette dernière. En s'insérant dans un emploi étudiant au Québec, nous évoquons les décalages culturels et professionnels auxquels nous avons fait face ; notamment la diversité générationnelle présente sur le lieu de travail. Les divergences d'âge, de principes, de modes de communication, de relation à l'autorité ou aux technologies qui ont parfois créé des frictions, mais également des chances d'apprentissage et d'évolution. Le travail de recherche établit une connexion entre ces observations pratiques et les contributions de la littérature académique concernant les traits distinctifs des différentes générations, leurs aspirations professionnelles et les défis liés à la coexistence intergénérationnelle. Nous soulignons aussi l'importance cruciale de la communication, du mentorat de la reconnaissance réciproque dans la gestion de cette diversité. Nos résultats indiquent également que, si elle est bien dirigée (leadership bienveillant), la collaboration intergénérationnelle peut encourager l'échange de connaissances, stimuler l'innovation et améliorer la performance du groupe. Ainsi, cette étude permet une meilleure appréhension des dynamiques humaines présentes dans les équipes intergénérationnelles et encourage les environnements professionnels à reconsidérer leurs méthodes afin de mieux valoriser cette richesse humaine. -- Mot(s) clé(s) en français : Équipe, Génération, Intergénérationnel, Autopraxéographie. -- ABSTRACT : This dissertation explores the challenges encountered when working within intergenerational teams in Quebec. To do so, we used the method of autopraxeography, a qualitative and introspective approach. This approach aims to examine one's own professional experience to generate knowledge based on it. By integrating into a student job in Quebec, we discuss the cultural and professional gaps we faced, particularly the generational diversity present in the workplace. Differences in age, principles, communication styles, and relationships with authority or technology sometimes created friction, but also opportunities for learning and advancement. This research establishes a connection between these practical observations and the contributions of the academic literature concerning the distinctive traits of different generations, their professional aspirations, and the challenges associated with intergenerational coexistence. We also emphasize the crucial importance of communication, mentoring, and mutual recognition in managing this diversity. Our findings also indicate that, if well-managed (benevolent leadership), intergenerational collaboration can encourage knowledge exchange, stimulate innovation, and improve group performance. Thus, this study provides a better understanding of the human dynamics present in intergenerational teams and encourages professional environments to reconsider their methods in order to better value this human wealth. -- Mot(s) clé(s) en anglais : Team, Generation, Intergeneration, Autopraxeography.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.951

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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 designQualitative
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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