An obsession with meaning : a critical examination of the pictograph sites of the Lake of Woods
Bibliographic record
Abstract
Most researchers who study rock image sites tend to be interested in the meaning of images, even though they could obtain more empirical information about these images and their physical location. Furthermore, very little of the work done in the past on rock image sites has been systematic. In this thesis I address the dearth of detailed information on the images and their context. This thesis presents a thorough examination of the images of the twenty-seven pictograph sites in the Lake of the Woods, in the Canadian Shield. These pictograph sites were selected because they exhibit traits evident in rock image studies in other parts of the world. This study is based on data collected during three months of fieldwork conducted in 2001. Images were found on cliff faces and inside caves. New images and new sites were found and identified. Here, as elsewhere, the choice of theoretical approach influences the fieldwork, analysis, and search for meaning. Each prescribes the types of questions asked and determines the levels of understanding obtained about whichever form of archaeological evidence is being considered. The different but complementary theoretical approaches should be employed in a definite order. The same data must be examined in sequential order using these different approaches to increase the potential quantity and quality of information gained. Archaeologists should use the following sequence of approaches: culture-historical, contextual, followed by either the homological, or analogical approaches, or a combination of the latter two. Classifying and describing any image is very difficult, since the level of description given to an image affects the way in which it can be analysed, and heavily influences the possible outcome of any discussion of perceived meaning. A rigorous examination of the images of these sites was conducted to (a) identify the possible vocabulary of images, (b) determine whether combinatory, rules exist, (c) reconstitute the life history of each site, and (d) ascertain whether the images can be related to other indigenous images to determine if this can provide information about the meaning(s) of the rock images. In assessing the meaning of the rock images, the images of a few birch bark scrolls were considered, since it was posited that a detailed investigation of the scrolls, the ethnographic record, and their pictographs might provide some answers regarding the meanings of the images found on the rock faces.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.023 | 0.052 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".