Bibliographic record
Abstract
In 2015, the article ‘Polis University as a Lean Startup Innovation’ was published in this journal. The present article is a sequel, researching how the follow-up phase after the initial startup has evolved. The purpose of this article is to: i) review Polis University management since the startup phase in terms of lean, entrepreneurial management, and ambidexterity; and ii) examine strategic directions for the future of the university. The article explains Polis University’s management of new and existing business opportunities, and observes that, like so many other organizations, daily business management and incremental improvement tends to receive the most attention. Ambidexterity is organized both structurally through the establishment of an Innovation Factory, and contextually – expecting from staff that they continuously improve their work. Regarding the university’s strategic direction, we conclude that Polis University adheres to the aim of bearing relevance for society, identified by European Commission for the future of universities. Polis University was started with the mission to be relevant for society – not only through providing education and conducting research, but also through its envisioned positive impact on Albanian and Western Balkan development.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".