Haptical Recording Experience: Exploring the iceberg model as a physical tool for decision-making in systemic design oriented leadership
Bibliographic record
Abstract
The iceberg model, derived from systems thinking, is one of the powerful methods for understanding the hidden dynamics and complexities that influence decision-making. By examining the various layers of the iceberg metaphorically, one can gain a deeper understanding of the interconnectedness and interdependencies within systems, enabling one to make more informed and connected decisions and foster sustainable change in a complex world. By employing the iceberg model, systemic design oriented leadership could gain a holistic understanding of the hidden dynamics that drive behaviours and outcomes within their organisations. Such leadership could even enhance a learning culture that encourages the exploration of mental models, challenges assumptions and promotes critical thinking. Through dialogue and reflection, leaders could surface underlying beliefs and values, thereby supporting a deeper understanding and empathy within the organisation. Leadership that embraces the iceberg model might effectively drive sustainable change. By focusing on mental models and systemic structures, systemic design oriented leaders can design interventions that address the root causes of challenges rather than applying quick fixes. This approach fosters a culture of continuous improvement and adaptability, positioning the organisation for long-term success. This paper explores the application of the iceberg model as a physical play in systemic design oriented leadership, highlighting its significance in identifying and addressing underlying factors that impact organisational behaviour and performance.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".