1 In Search of the Meaning of Games in Life: A Journey to the Arctic of Norway
Bibliographic record
Abstract
Throughout history the games we have played have been a testament about who we were, and are. From early Inuit bone and hunting games, to the gladiator contests of Ancient Rome, to the modern American game of baseball, the games we play have served as a statement of and a rehearsal for the life-world of that period and place. By reconnecting with and understanding the games of our past, we can build meaningful bridges between our past and present, and hopefully gain a better understanding of the meaning and importance of the modern games we play. The aforesaid are timely and important, especially as they relate to indigenous people throughout the world who are trying to preserve their traditions in our modern world. Israel Ruong (1953) called the preservation of indigenous Sámi traditions, “active adaptation. ” He said, “Active adaptation means that Sámi cannot alone and without criticism adopt modern culture, casting aside their culture’s irreplaceable values, but that they hold fast to their cultural traditions in the new conditions (Ruong cited in Lehtola, 2004: p. 60). For much of my adult life I have had a passion for trying to understand the deeper meanings of the games we play. This passion emerged during my tenure as an assistant coach (Dance Conditioning) with the 1982-84 Philadelphia Seventy-Sixers Basketball Team (1983 World
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".