The Death Of High Performance Programs: Transferring Knowledge In The New Millennial
Notice bibliographique
Résumé
The “Opportunity Gap” and the “Skills Gap” have been the focus of conferences this year. Solutions have been proposed, but none has aligned technology with the cultural capital that exists within an organization to close the gaps. Cultural capital is the accumulation of symbols, language, political knowledge and expertise that senior leaders use to get the job done daily within an organization. Emerging leaders who have not been immersed in this cultural capital, particularly diverse candidates, struggle to identify or find the resources and develop the skills necessary to succeed in an organization. Frequently training dollars are allocated to high-potential candidates who often are able to assimilate these skills quickly, but rarely if ever are they, when at the managerial level, tested on their ability to transfer these skills to others. This inability of high-potential mangers to transfer cultural capital between the different layers of an organization is a real cause of the skills and opportunity gap.In 2012, at a larger retailer in the southern US, we began testing a pilot program for a practical management training in the form of a Licensed Online Open Course (modeled after the MOOC) to determine if managers with access to a structured and disciplined curriculum could have an impact on human capital productivity and increase retention among managers and their followers. The LOOC and its content were created by the Donohue Mentoring System™ (DMS).We concluded that on-the-job training, using the DMS LOOC delivery and mentoring, fosters cultural capital, increased job motivation and satisfaction, and subsequently increased output (productivity) by 10%. Using a survey comprised of 19 questions, we were also able to determine that trust in superior officers increased by 10% and self-assessed value of work increased by 10%. These increases resulted in a reduced employee churn of 50%. As we carried on pilots in 2013, 2014 and 2015 with other large corporations, not for profit and a government, we were surprised to find that the system delivered the same noteworthy results each time. We found that when managers are presented with a technology solution that allows them to train candidates (mentees) and transfer knowledge to their mentees, leadership magic happens, and people engage with each other. What surprised us the most was that no matter the size of the pilot program, mentees for the most part surpassed their mentors’ knowledge pre-program.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».