The shifting baselines syndrome: perception, deception, and the future of our oceans
Bibliographic record
Abstract
INTRODUCTION Humans consider the surroundings of their youth as natural and, as they age, recognize the changes to their environment as unnatural. Children repeat the errors of their parents. Thus, as each new generation collectively adopts this perverse perspective, we lose track of the inexorable degradation of native ecosystems. Pauly (1995) coined the term “shifting baselines syndrome” to describe this phenomenon in relation to the problem of fisheries, for which baselines for so-called “pristine” populations are established by managers that ignore the impacts of earlier fishing that had already greatly reduced fish abundance and size. Consequently, expectations change as we shift to eating smaller and smaller fish and invertebrates at progressively lower trophic levels – the phenomenon now commonly referred to as “fishing down the food web” (Pauly et al ., 1998). The gradual accommodation of loss applies to a host of other ecological and cultural resources. Pollan (2008) writes on “the tangible material formerly known as food” and notes that many modern food systems would be unrecognizable to people only a few decades ago. Similarly, we are in the process of destroying many indigenous cultures through a combination of genocide and assimilation. We lose one of the 7000 languages remaining on Earth every two weeks (Wilford, 2007). The “future of the past” (Stille, 2002) is grim indeed. It is not sycophantic to say that the idea of shifting baselines was revolutionary for the field of ecology, for which our limited understanding of patterns of distribution and abundance, food webs, and community structure are based on the assumption that what we can observe today is all that matters (Jackson, 1997, 2006).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".