Bibliographic record
Abstract
A number of people have had a significant role in the completion of this doctoral thesis.I have had a great privilege to do research surrounded by many talented and pleasant scholars at Turku School of Economics.In 1999 Professor Paavo Okko offered me a position in a research project studying economic development of various Finnish regions.At that time I was not yet fully motivated to do doctoral studies.With his admirable and tolerant attitude Paavo let my motivation grow in peace and offered me some further research projects.Eventually, I found the motivation and seriously started to aim at a doctoral degree in the summer of 2002.All the time Paavo has supported and encouraged me and showed belief in me even though it took several years before I seriously started the studies.Without Paavo's patience and support I would probably never have completed the doctoral thesis.I will always be grateful to Paavo.The real estate economics course lectured by Asko Miettilä raised my initial interest in real estate markets.Asko was the person who originally introduced me to Professor Okko.Asko's influence has been of great importance to the commencement of this thesis.Doctor Hannu Kahra, in turn, has had a great impact on my interest in empirical analysis employing time series econometrics.I took part in Hannu's stimulating course in time series econometrics at the beginning of my doctoral studies.Since then Hannu has given me guidance in various questions concerning econometrics and as a colleague he has been most inspiring and pleasant.Furthermore, I am in gratitude to Professor Heikki A.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".