The Death Of High Performance Programs: Transferring Knowledge In The New Millennial
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".