Bibliographic record
Abstract
This research has explored the status of computerization in architectural firms and the problems they face in adopting and using computers. The research methodology included both, a literature search and case studies consisting of interviews and questionnaires. To gain an in-depth understanding of the status of computer use and its related problems, and to benefit from the experience of current computer owner/users, eleven Vancouver firms which currently use computers in their practice, are interviewed. The initial decision to computerize is often based on a group of perceptions from the benefits of computer use for the practice. This decision is usually rationalized by the need to remain competitive in the market, to increase the productivity or to respond to client's/project's requirements. The extent of planning for the process of computerization usually depends on the size of the practice and scope of computerization. Planning however, is typically short term and problems and needs are addressed as and when they occur. Most architects select their hardware first and then their application software. The typical approach at this stage is to rely mainly on in-house resources and to select the system mainly according to price. The issues related to implementation and use of the system are usually addressed stage by stage. In attempting successful implementation and computer use, the impact of management style and staffs attitudes appear to be significant. In most firms there is not any methods of evaluation to identify and modify the problems and therefore increase the effectiveness of computer use in the practice. System expansion is in general due to satisfactory experience, or an initial under estimate of station requirements. This stage is often based on a more realistic understanding of both, the firm's requirements and the computers capabilities. The most important observation is that the validity of the advantages of computerization are not examined at the initial stages nor are methods of increasing and achieving them. In addition, revenue increase through the expansion of services is seldom considered. Following the research, a series of guidelines are developed for practising architects, suggesting that advance planning can reduce most problems or their impacts. These guidelines present some important factors to be considered in the process of computerization were developed. They are structured according to the stages of computerization.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".